Professor Grigorios Antoniou

Most of the existing work in the field of Qualitative Spatial Temporal Reasoning (QSTR) has focussed on comparatively small constraint networks that consist of hundreds or at most thousands of relations. Recently we have seen the emergence of much larger qualitative spatial knowledge graphs that feature hundreds of thousands and millions of relations. Traditional approaches to QSTR are unable to reason over networks of such size. In this article we describe ParQR, a parallel, distributed implementation of QSTR techniques that addresses the challenge of reasoning over large-scale qualitative spatial and temporal datasets. We have implemented ParQR using the Apache Spark framework, and evaluated our approach using both large scale synthetic datasets and real-world knowledge graphs. We show that our approach scales effectively, is able to handle constraint networks consisting of millions of relations, and outperforms current distributed implementations of QSTR.

The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time (referred to as dynamic information) and of their properties in terms of qualitative descriptions in addition to quantitative ones (i.e., dates, time instants and intervals). For this purpose, we advocate the use of natural language expressions, such as before or after, for temporal entities whose exact durations or starting and ending points in time are unknown. Reasoning over all types of temporal information (such as the above) is also an important research problem. The current work addresses all these issues as follows: The representation of dynamic concepts is achieved using the 4D-fluents or, alternatively, the N-ary relations mechanism. Both mechanisms are thoroughly explored and are expanded for representing qualitative and quantitative temporal information in OWL. In turn, temporal information is expressed using either intervals or time instants. Qualitative temporal information representation in particular, is realized using sets of SWRL rules and OWL axioms leading to a sound, complete and tractable reasoning procedure based on path consistency applied on the existing relation sets. Building upon existing Semantic Web standards (OWL), tools and member submissions (SWRL), as well as integrating temporal reasoning support into the proposed representation, are important design features of our approach.

Abstract

Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.

Dyslexia presents significant challenges in education for stu- dents worldwide. While assistive technologies have been used to enhance readability, no study has systematically evaluated the ability of Language Models (LMs) to generate dyslexia-friendly text aligned with established accessibility guidelines. This proof-of-concept study assesses three state- of-the-art LMs on their ability to identify and apply dyslexia-friendly text criteria. Our findings reveal that their knowledge is limited and poses potential risks. To address this, we introduce DysT ext, a novel metric that quantifies dyslexia-friendly text characteristics based on the British Dyslexia Association’s Dyslexia Style Guide. Results indicate that while LMs can enhance the dyslexia-friendliness of texts, their responses should not be blindly trusted, underscoring the need for further verification.

Large language models (LLMs) such as ChatGPT have risen in prominence recently, leading to the need to analyze their strengths and limitations for various tasks. The objective of this work was to evaluate the performance of large language models for model checking, which is used extensively in various critical tasks such as software and hardware verification. A set of problems were proposed as a benchmark in this work and three LLMs (GPT-4, Claude, and Gemini) were evaluated with respect to their ability to solve these problems. The evaluation was conducted by comparing the responses of the three LLMs with the gold standard provided by model checking tools. The results illustrate the limitations of LLMs in these tasks, identifying directions for future research. Specifically, the best overall performance (ratio of problems solved correctly) was 60%, indicating a high probability of reasoning errors by the LLMs, especially when dealing with more complex scenarios requiring many reasoning steps, and the LLMs typically performed better when generating scripts for solving the problems rather than solving them directly.

In this paper we consider how Qualitative Spatial Reasoning (QSR) can be used to answer queries over large-scale knowledge graphs such as YAGO and DBPedia. We describe the challenges associated with spatially querying knowledge graphs such as point based representations, sparsity of qualitative relations, and scale. We address these challenges and present a query engine, Parallel Qualitative Reasoner-Query Engine (ParQR-QE), that uses a novel distributed qualitative spatial reasoning algorithm to provide answers to GeoSPARQL queries. An experimental evaluation using a range of different query types and the YAGO knowledge graph shows the advantages of QSR techniques in comparison to purely quantitative approaches.

Large Language Models such as ChatGPT have risen in prominence recently leading to the need to analyse their strengths and limitations on various tasks. The objective of this work is to evaluate the performance of Large Language Models on Model Checking which is used extensively in various critical tasks such as software and hardware verification.

Managing supply chain risks has received increased attention in recent years, aiming to shield supply chains from disruptions by predicting their occurrence and mitigating their adverse effects. At the same time, the resurgence of Artificial Intelligence (AI) has led to the investigation of machine learning techniques and their applicability in supply chain risk management. However, most works focus on prediction performance and neglect the importance of interpretability so that results can be understood by supply chain practitioners, helping them make decisions that can mitigate or prevent risks from occurring. In this work, we first propose a supply chain risk prediction framework using data-driven AI techniques and relying on the synergy between AI and supply chain experts. We then explore the trade-off between prediction performance and interpretability by implementing and applying the framework on the case of predicting delivery delays in a real-world multi-tier manufacturing supply chain. Experiment results show that prioritising interpretability over performance may require a level of compromise, especially with regard to average precision scores.

Argumentation is a prominent AI research area, focused on approaches and techniques for performing common-sense reasoning, that is of paramount importance in a wide range of real-world applications, such as decision support and recommender systems. In this work we introduce an approach for updating an abstract Argumentation Framework (AF) so that achievement with respect to a given set of goals is maximised. The set of goals identifies arguments for which a specific acceptability status (a labelling) will be pursued, distinguishing between “in” and “out” goals. Given an AF, a set of goals and a set of available actions allowing to add or remove arguments and attacks from the AF, our approach will select the strategy (set of actions) that should be applied in order to obtain a new AF where the goals achievement is maximised. Moreover, the selected strategy will be optimal with respect to the number of actions to be applied. In the context of argumentation-based expert and intelligent systems, our approach will provide tools allowing the user to interact with the argumentative reasoning process carried out by the system, learning how the strategy she undertakes will affect the recommendations she receives. For that, we propose an encoding of the AF, the available actions and goals as weighted Boolean formulas, and rely on MaxSAT techniques for selecting the optimal strategy. We provide an experimental analysis of our approach, and formally show that the results we obtain correspond to the optimal strategy.

Anomalies such as redundant, contradictory, or deficient knowledge in a knowledge base indicate possible errors. Various methods for detecting such anomalies have been introduced, analyzed, and applied in the past years, but they usually deal with rule-based systems. So far, little attention has been paid to the verification and validation of more complex representations, such as nonmonotonic knowledge bases, although there are good reasons to expect that these technologies will be increasingly used in practical applications. This article does a step towards the verification of knowledge bases which include defaults by providing a theoretical foundation of correctness concepts and a classification of possible anomalies. It also points out how existing verification methods may be applied to detect some anomalies in nonmonotonic knowledge bases, and discusses methods of avoiding potential inconsistencies (in the context of default reasoning inconsistency means nonexistence of extensions). © 1997 John Wiley & Sons, Inc.

Abstract

Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

Mental illnesses are becoming increasingly prevalent, in turn leading to an increased interest in exploring artificial intelligence (AI) solutions to facilitate and enhance healthcare processes ranging from diagnosis to monitoring and treatment. In contrast to application areas where black box systems may be acceptable, explainability in healthcare applications is essential, especially in the case of diagnosing complex and sensitive mental health issues. In this paper, we first summarize recent developments in AI research for mental health, followed by an overview of approaches to explainable AI and their potential benefits in healthcare settings. We then present a recent case study of applying explainable AI for ADHD diagnosis which is used as a basis to identify challenges in realizing explainable AI solutions for mental health diagnosis and potential future research directions to address these challenges.

Improving a city's infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment is that this may enable the use of intelligent processes to autonomously manage urban facilities such as traffic signal controls. In this paper we document an attempt to integrate sets of sensor and historical data using a data hub and a set of ontologies for the data. We argue that access to such high level integrated data sources leads to the enhancement of the capabilities of an urban transport operator. We demonstrate this by documenting the development of a planning agent which uses such data as inputs in the form of logic statements, and when given traffic goals to achieve, outputs complex traffic signal strategies which help transport operators deal with exceptional events such as road closures or road traffic saturation. The aim is to create an autonomous agent which reacts to commands from transport operators in the face of exceptional events involving saturated roads, and creates, executes and monitors plans to deal with the effects of such events. We evaluate the intelligent agent in a region of a large urban area, under the direction of urban transport operators.

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.

Abstract. Background: Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide risk has proven extremely difficult to assess for medical specialists, and traditional methodologies deployed have been ineffective. Advances in machine learning make it possible to attempt to predict suicide with the analysis of relevant data aiming to inform clinical practice. Aims: We aimed to (a) test our artificial intelligence based, referral-centric methodology in the context of the National Health Service (NHS), (b) determine whether statistically relevant results can be derived from data related to previous suicides, and (c) develop ideas for various exploitation strategies. Method: The analysis used data of patients who died by suicide in the period 2013–2016 including both structured data and free-text medical notes, necessitating the deployment of state-of-the-art machine learning and text mining methods. Limitations: Sample size is a limiting factor for this study, along with the absence of non-suicide cases. Specific analytical solutions were adopted for addressing both issues. Results and Conclusion: The results of this pilot study indicate that machine learning shows promise for predicting within a specified period which people are most at risk of taking their own life at the time of referral to a mental health service.

Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand the deployment of interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based models are generally considered interpretable that are able to reflect the associations between medical conditions and associated symptoms, through the use of linguistic if-then statements. Systems built on top of fuzzy sets are of particular appealing to medical applications since they enable the tolerance of vague and imprecise concepts that are often embedded in medical entities such as symptom description and test results. They facilitate an approximate reasoning framework which mimics human reasoning and supports the linguistic delivery of medical expertise often expressed in statements such as ‘weight low’ or ‘glucose level high’ while describing symptoms. This paper proposes an approach by performing data-driven learning of accurate and interpretable fuzzy rule bases for clinical decision support. The approach starts with the generation of a crisp rule base through a decision tree learning mechanism, capable of capturing simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the framework of adaptive network-based fuzzy inference system (ANFIS), thereby further optimising the parameters of both rule antecedents and consequents. Experimental studies on popular medical data benchmarks demonstrate that the proposed work is able to learn compact rule bases involving simple rule antecedents, with statistically better or comparable performance to those achieved by state-of-the-art fuzzy classifiers.

Abstract

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that includes symptoms such as inattentiveness, hyperactivity and impulsiveness. It is considered as an important public health issue and prevalence of, as well as demand for diagnosis, has increased as awareness of the disease grew over the past years. Supply of specialist medical experts has not kept pace with the increasing demand for assessment, both due to financial pressures on health systems and the difficulty to train new experts, resulting in growing waiting lists. Patients are not being treated quickly enough causing problems in other areas of health systems (e.g. increased GP visits, increased risk of self-harm and accidents) and more broadly (e.g. time off work, relationship problems). Advances in AI make it possible to support the clinical diagnosis of ADHD based on the analysis of relevant data. This paper reports on findings related to the mental health services of a specialist Trust within the UK’s National Health Service (NHS). The analysis studied data of adult patients who underwent diagnosis over the past few years, and developed a hybrid approach, consisting of two different models: a machine learning model obtained by training on data of past cases; and a knowledge model capturing the expertise of medical experts through knowledge engineering. The resulting algorithm has an accuracy of 95% on data currently available, and is currently being tested in a clinical environment.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that includes symptoms such as inattentiveness, hyperactivity and impulsiveness. It is considered as an important public health issue, and prevalence of diagnosis has increased as awareness of the disease grew over the past years. Supply of specialist medical experts has not kept pace with the increasing demand for assessment, both due to financial pressures on health systems and the difficulty to train new experts, resulting in growing waiting lists. Patients are not being treated quickly enough causing problems in other areas of health systems (e.g. increased GP visits, increased risk of self-harm and accidents) and more broadly (e.g. time off work, relationship problems). Advances in machine learning make it possible to attempt to diagnose ADHD based on the analysis of relevant data, and this could inform clinical practice. This paper reports on findings related to the mental health services of a specialist Trust within the UK’s National Health Service (NHS). The analysis studied data of adult patients who underwent diagnosis over the past few years, and developed a diagnostic model for ADHD in adults. The results demonstrate that it is indeed possible to correctly diagnose ADHD patients with promising statistical accuracy.

Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing range of scientific disciplines, as well as business and human activities, are becoming data-driven. This chapter summarises existing research on legal representation and reasoning in order to uncover technical challenges associated both with the integration of rules and databases and with the main concepts of the big data landscape. We expect these challenges lead naturally to future research directions towards achieving large scale legal reasoning with rules and databases.

This chapter considers the importance of decision support systems for supply chain risk management (SCRM). The first part provides an overview of the different operations research techniques and methodologies for decision making for managing risks, focusing on multiple-criteria decision analysis methods and mathematical programming. The second part is devoted to artificial intelligence (AI) techniques which have been applied in the SCRM domain to analyse data and make decisions regarding possible risks. These include Petri nets, multi-agent systems, automated reasoning and machine learning. The chapter concludes with a discussion of potential ways in which future decision support systems for SCRM can benefit from recent advances in AI research.

Heart disease, caused by low heart rate, is one of the most significant causes of mortality in the world today. Therefore, it is critical to monitor heart health by identifying the deviation in the heart rate very early, which makes it easier to detect and manage the heart’s function irregularities at a very early stage. The fast-growing use of advanced technology such as the Internet of Things (IoT), wearable monitoring systems and artificial intelligence (AI) in the healthcare systems has continued to play a vital role in the analysis of huge amounts of health-based data for early and accurate disease detection and diagnosis for personalized treatment and prognosis evaluation. It is then important to analyze the effectiveness of using data analytics and machine learning to monitor and predict heart rates using wearable device (accelerometer)-generated data. Hence, in this study, we explored a number of powerful data-driven models including the autoregressive integrated moving average (ARIMA) model, linear regression, support vector regression (SVR), k-nearest neighbor (KNN) regressor, decision tree regressor, random forest regressor and long short-term memory (LSTM) recurrent neural network algorithm for the analysis of accelerometer data to make future HR predictions from the accelerometer’s univariant HR time-series data from healthy people. The performances of the models were evaluated under different durations. Evaluated on a very recently created data set, our experimental results demonstrate the effectiveness of using an ARIMA model with a walk-forward validation and linear regression for predicting heart rate under all durations and other models for durations longer than 1 min. The results of this study show that employing these data analytics techniques can be used to predict future HR more accurately using accelerometers.

The importance of transformations and normal forms in logic programming, and generally in computer science, is well documented. This paper investigates transformations and normal forms in the context of Defeasible Logic, a simple but efficient formalism for nonmonotonic reasoning based on rules and priorities. The transformations described in this paper have two main benefits: on one hand they can be used as a theoretical tool that leads to a deeper understanding of the formalism, and on the other hand they have been used in the development of an efficient implementation of defeasible logic.

Large-scale analytics is a key application area for data processing and parallel computing research. One of the most common (and challenging) operations in this domain is the join. Though inner join approaches have been extensively evaluated in parallel and distributed systems, there is little published work providing analysis of outer joins, especially in the extremely popular cloud computing environments. A common type of outer join is the small–large outer join, where one relation is relatively small and the other is large. Conventional implementations on this condition, such as one based on hash redistribution, often incur significant network communication, while the duplication-based approaches are complex and inefficient. In this work, we present a new method called DDR (duplication and direct redistribution), which aims to enable efficient small–large outer joins in cloud computing environments while being easy to implement using existing predicates in data processing frameworks. We present the detailed implementation of our approach and evaluate its performance through extensive experiments over the widely used MapReduce and Spark platforms. We show that the proposed method is scalable and can achieve significant performance improvements over the conventional approaches. Compared to the state-of-art method, the DDR algorithm is shown to be easier to implement and can achieve very similar or better performance under different outer join workloads, and thus, can be considered as a new option for current data analysis applications. Moreover, our detailed experimental results also have provided insights of current small–large outer join implementations, thereby allowing system developers to make a more informed choice for their data analysis applications.

Conservative extensions of logical theories play an important role in software engineering. They provide a formal basis for program refinement and guarantee the integrity and transparency of modules and objects. This paper studies conservative extension ideas in the context of default logic. In particular, we define several alternative concepts, study their properties, and derive interconnections among the concepts. The main result provides an interesting distinctive feature of Reiter’s default logic over some well-known variants.

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumptions that fluents persist and actions have effects on the subsequent situation only. We provide a solution based on an extension of the situation calculus and the work of McCain and Turner. More specifically, we study the case where there are conflicting effects of concurrently executing actions and we distinguish between hard and soft integrity constraints. © 2007 IEEE.

The first source of motivation for our work is the modelling of multi-agent systems. In particular, we follow the approach of [1] that combines two perspectives: (a) a cognitive account of agents that specifies motivational attitudes, using the BDI architecture [2], and (b) modelling of agent societies by means of normative concepts [3].

The standardization of the Semantic Web has reached as far as ontologies and ontology languages. However, in order for the full potential of the Semantic Web to be achieved, the ability of reasoning over the available information is also essential. Rules can assist in this affair and various logics have been proposed for the Semantic Web domain. One of them is defeasible reasoning that deals with incomplete and conflicting information. However, despite its solid mathematical notation, it may be confusing to end users. To confront this downside, we proposed a representation schema for defeasible logic rule bases, which is based on directed graphs that feature distinct node and connection types. This paper presents DR-VisMo, a defeasible logic rule base editor and visualization system that implements this representation approach. The system also features a stratification algorithm for visualizing rule bases that deals with decisions, regarding the arrangement of the various elements in the graph. DR-VisMo is implemented as part of VDR-DEVICE, an environment for modeling and deploying defeasible logic rule bases on top of RDF ontologies.

This work presents a visualization algorithm for defeasible logic rule bases as well as a software tool that applies this algorithm, according to which, a directed graph is produced that represents the rule base. The graph features distinct node types for rules and atomic formulas and distinct connection types for the various rule types of defeasible logic. © Springer-Verlag Berlin Heidelberg 2007.

An essential task in managing description logic (DL) ontologies is the elimination of problematic axioms. Such elimination is formalised as the operation of contraction in belief change. In this paper, we investigate contraction over DL-LiteR TBoxes. In belief change, a well known approach for defining contraction is via epistemic entrenchments which are preference orderings over formulas. The approach however is not applicable in DL-LiteR as classic belief change assumes an underlying logic that is different from DL-LiteR. Thus we reformulate the epistemic entrenchment approach to make it applicable to DL-LiteR.We then provide instantiation for the reformulated approach.

Forgetting is an important tool for reducing ontologies by eliminating some redundant concepts and roles while preserving sound and complete reasoning. Attempts have previously been made to address the problem of forgetting in relatively simple description logics (DLs), such as DL‐Lite and extended . However, the issue of forgetting for ontologies in more expressive DLs, such as and OWL DL, is largely unexplored. In particular, the problem of characterizing and computing forgetting for such logics is still open. In this paper, we first define semantic forgetting about concepts and roles in ontologies and state several important properties of forgetting in this setting. We then define the result of forgetting for concept descriptions in , state the properties of forgetting for concept descriptions, and present algorithms for computing the result of forgetting for concept descriptions. Unlike the case of DL‐Lite, the result of forgetting for an ontology does not exist in general, even for the special case of forgetting in TBoxes. This makes the problem of computing the result of forgetting in more challenging. We address this problem by defining a series of approximations to the result of forgetting for ontologies and studying their properties. Our algorithms for computing approximations can be directly implemented as a plug‐in of an ontology editor to enhance its ability of managing and reasoning in (large) ontologies.

For many years, the non-monotonic reasoning community has focussed on highly expressive logics. Such logics have turned out to be computationally expensive, and have given little support to the practical use of non-monotonic reasoning. In this work we discuss defeasible logic, a less-expressive but more efficient non-monotonic logic. We report on two new implemented systems for defeasible logic: a query answering system employing a backward chaining approach, and a forward-chaining implementation that computes all conclusions. Our experimental evaluation demonstrates that the systems can deal with large theories (up to hundreds of thousands of rules). We show that defeasible logic has linear complexity, which contrasts markedly with most other non-monotonic logics and helps to explain the impressive experimental results. We believe that defeasible logic, with its efficiency and simplicity is a good candidate to be used as a modelling language for practical applications, including modelling of regulations and business rules. © 2000 IEEE.

Abstract

Data originating from the Web, sensor readings and social media result in increasingly huge datasets. The so called Big Data comes with new scientific and technological challenges while creating new opportunities, hence the increasing interest in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how the well-founded semantics can process huge amounts of data through mass parallelization. More specifically, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that well-founded semantics can be applied to billions of facts. To the best of our knowledge, this is the first work that addresses large scale nonmonotonic reasoning without the restriction of stratification for predicates of arbitrary arity.

This paper provides the foundation of connections between default reasoning and constraint satisfaction. Such connections are important because they combine fields with different strengths that complement each other: default reasoning is broadly seen as a promising method for reasoning from incomplete information, but is hard to implement. On the other hand, constraint satisfaction has evolved as a powerful, and efficiently implementable, problem solving paradigm in artificial intelligence. In this paper, we show how THEORIST knowledge bases and theories in Constrained Default Logic with prerequisite-free defaults may be mapped to partial constrained satisfaction problems. We also extend these results to deal with priorities among defaults.

Representation of spatial information for the Semantic Web often involves qualitative defined information (i.e., information described using natural language terms such as "Left"), since precise arithmetic descriptions using coordinates and angles are not always available. A basic aspect of spatial information is directional relations, thus embedding directional spatial relations into ontologies along with their semantics and reasoning rules is an important practical issue. This work proposes a new representation for directional spatial information in ontologies by means of OWL properties and reasoning rules in SWRL embedded into the ontology. The proposed representation is based on the combination of object orientations (e.g., same direction or opposite) and cone shaped directional relations of positions using an egocentric reference (e.g., left or right of an object). The proposed representation is to the best of our knowledge a novel one, and in this work, the proposed representation is analysed, implemented and evaluated. © Springer International Publishing Switzerland 2015.

Representation of temporal information for the Semantic Web often involves qualitative defined information (i.e., information described using natural language terms such as "before" or "lasts longer than"), since precise dates, times and durations are not always available. A basic aspect of temporal information is duration of intervals, thus embedding duration relations into ontologies along with their semantics and reasoning rules is an important practical issue. This work proposes a new representation for intervals and their durations in ontologies by means of OWL properties and reasoning rules in SWRL embedded into the ontology. The proposed representation is based on the decomposition of Interval Duration calculus relations (INDU) offering a compact representation and a tractable reasoning mechanism. Furthermore, by embedding reasoning rules using SWRL into the ontology, reasoning semantics are an integrated part of the representation which can be easily shared and modified without requiring additional specialized reasoning software. © Springer International Publishing Switzerland 2015.

The ultimate vision of the Semantic Web (SW) is to offer an interoperable and information-rich web environment that will allow users to safely delegate complex actions to intelligent agents. Much work has been done for agents' interoperability; a plethora of proposals and standards for ontology-based metadata and rule-based reasoning are already widely used. Nevertheless, the SW proof layer has been neglected so far, although it is vital for SW agents and human users to understand how a result came about, in order to increase the trust in the interchanged information. This paper focuses on the implementation of third party SW reasoning and proofing services wrapped as agents in a multiagent framework. This way, agents can exchange and justify their arguments without the need to conform to a common rule paradigm. Via external reasoning and proofing services, the receiving agent can grasp the semantics of the received rule set and check the validity of the inferred results. © Springer-Verlag Berlin Heidelberg 2011.

This paper describes a context-aware meeting alert, which aims at alerting the user in time about upcoming scheduled calendar events, considering the state of the user's context. This application integrates semantic web technology in RDF (for representing calendars), semantic web rules (for making a context dependent decision about the precise timing of the alert), and mobile technology for location sensing and message delivery. The outlined work is an experiment seeking to demonstrate the feasibility of applying efficient, semantically sound semantic web reasoning to mobile applications. © 2007, Inderscience Publishers.

In this paper a system for storing and querying medical RDF data using Hadoop is developed. This approach enables us to create an inherently parallel framework that will scale the workload across a cluster. Unlike existing solutions, our framework uses highly optimised joining strategies to enable the completion of eight separate SPAQL queries, comprised of over eighty distinct joins, in only two Map/Reduce iterations. Results are presented comparing an optimised version of our solution against Jena TDB, demonstrating the superior performance of our system and its viability for assessing the quality of medical data.

Increasingly huge amounts of data are published on the Web, and generated from sensors and social media. This Big Data challenge poses new scientific and technological challenges and creates new opportunities - thus the increasing attention in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how stratified semantics of logic programming, equivalent to the well-founded semantics for stratified programs, can process huge amounts of data through mass parallelization. In particular, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that stratified semantics of logic programming can be applied to billions of facts. © 2013 Springer-Verlag Berlin Heidelberg.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. This paper describes these scenarios in more detail, and reports on the implementation of a system for defeasible reasoning on the Web. The system (a) is syntactically compatible with RuleML; (b) features strict and defeasible rules and priorities; (c) is based on a translation to logic programming with declarative semantics; and (d) is flexible and adaptable to different intuitions within defeasible reasoning. © 2004 Springer-Verlag Berlin/Heidelberg.

Methods for dealing with a Horn logic program and one goal are well-known and successful. Here we are concerned with treating logic programa enhanced by some negative literals using the same methods, in particular SLD-resolutlon. We describe the approach and show it. correctness. The result can be applied to default reasoning and has some relevance for model elimination based theorem proving. © 1994 IGPL.

The success of the Semantic Web is impossible without any form of modularity, encapsulation, and access control. In an earlier paper, we extended RDF graphs with weak and strong negation, as well as derivation rules. The ERDF #n-stable model semantics of the extended RDF framework (ERDF) is defined, extending RDF(S) semantics. In this paper, we propose a framework for modular ERDF ontologies, called modular ERDF framework, which enables collaborative reasoning over a set of ERDF ontologies, while support for hidden knowledge is also provided. In particular, the modular ERDF stable model semantics of modular ERDF ontologies is defined, extending the ERDF #n-stable model semantics. Our proposed framework supports local semantics and different points of view, local closed-world and open-world assumptions, and scoped negation-as-failure. Several complexity results are provided. © 2013 Springer Science+Business Media Dordrecht.

Default logic is a prominent rigorous method for reasoning with incomplete information based on assumptions. It is a static reasoning approach, in the sense that it doesn't reason about changes and their consequences. On the other hand, its nonmonotonic behavior appears when changes to a default theory are made. This paper studies the dynamic behavior of default logic in the face of changes. We consider the operations of contraction and revision, present several solutions to these problems, and study their properties. © 2002 Wiley Periodicals, Inc.

In this work we present the design and implementation of a system for proof explanation in the Semantic Web, based on defeasible reasoning. Trust is a vital feature for Semantic Web. If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs. Our system produces automatically proof explanations using a popular logic programming system (XSB), by interpreting the output from the proof's trace and converting it into a meaningful representation. It also supports an XML representation (a RuleML language extension) for agent communication, which is a common scenario in the Semantic Web. The system in essence implements a proof layer for nonmonotonic rules on the Semantic Web. © Springer-Verlag Berlin Heidelberg 2007.

Logic and proofs constitute key factors in increasing the user trust towards the Semantic Web. Defeasible reasoning is a useful tool towards the development of the Logic layer of the Semantic Web architecture. However, having a solid mathematical notation, it may be confusing to end users, who often need graphical trace and explanation mechanisms for the derived conclusions. In a previous work of ours, we outlined a methodology for representing defeasible logic rules, utilizing directed graphs that feature distinct node and connection types. However, visualizing a defeasible logic rule base also involves the placement of the multiple graph elements in an intuitive way, a non-trivial task that aims at improving user comprehensibility. This paper presents a stratification algorithm for visualizing defeasible logic rule bases that query and reason about RDF data as well as a tool that applies this algorithm. © 2007 IEEE.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of logic programs. In particular, we give a translation of a defeasible theory D into a meta-program P(D). We show that under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead. © 2006 Cambridge University Press.

For a long time, logic programming and rule-based reasoning have been proposed as a basis for policy specification languages. However, the term “policy” has not been given a unique meaning. In fact, it is used in the literature in a broad sense that encompasses the following notions: Security Policies pose constraints on the behaviour of a system. They are typically used to control permissions of users/groups while accessing resources and services.Trust Management policy languages are used to collect user properties in open environments, where the set of potential users spans over the entire web.Action Languages are used in reactive policy specification to execute actions like event logging, notifications, etc. Authorizations that involve actions and side effects are sometimes called provisional.Business Rules are “statements about how a business is done” [25] and are used to formalize and automatize business decisions as well as for efficiency reasons. They can be formulated as reaction rules, derivation rules, and integrity constraints [142],[147].

Default logic is one of the most prominent approaches to nonmonotonic reasoning, and allows one to make plausible conjectures when faced with incomplete information about the problem at hand. Default rules prevail in many application domains such as medical and legal reasoning.

Several variants have been developed over the past year, either to overcome some perceived deficiencies of the original presentation, or to realize somewhat different intuitions. This paper provides a tutorial-style introduction to some important approaches of Default Logic. The presentation is based on operational models for these approaches, thus making them easily accessible to a broader audience, and more easily usable in practical applications.

This paper reports on a system for automated agent negotiation. It uses the JADE agent framework, and its major distinctive feature is the use of declarative negotiation strategies. The negotiation strategies are expressed in a declarative rules language, defeasible logic and are applied using the implemented defeasible reasoning system DR-DEVICE. The choice of defeasible logic is justified. The overall system architecture is described, and a particular negotiation case is presented in detail. © 2005 IEEE.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. This paper describes these scenarios in more detail, and reports on the implementation of a system for defeasible reasoning on the Web. The system is called DR-DEVICE and is capable of reasoning about RDF metadata over multiple Web sources using defeasible logic rules. The system is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The paper also briefly presents a semantic web broker example for apartment renting. © 2004 Springer-Verlag Berlin/Heidelberg.

In this paper, we discuss scalable methods for nonmonotonic rule-based reasoning over Semantic Web Data, using MapReduce. This work is motivated by the recent unparalleled explosion of available data coming from the Web, sensor readings, databases, ontologies and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application or domain specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. Our results indicate that our method shows good scalability properties and is able to handle a benchmark dataset of 1 billion triples, bringing it on par with state-of-the-art methods for monotonic reasoning on the semantic web. © 2014 ACM.

In this paper, we are presenting a scalable method for nonmonotonic rule-based reasoning over Semantic Web Data, using MapReduce. Our work is motivated by the recent unparalleled explosion of available data coming from the Web, sensor readings, databases, ontologies and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. We present a scalable MapReduce-based method for reasoning using defeasible stratified logics. Our results indicate that our method shows good scalability properties and is able to handle a benchmark dataset of 1 billion triples, bringing it on par with state-of-the-art methods for monotonic logics.

RDF/S ontologies are often used in e-science to express domain knowledge regarding the respective field of investigation (e.g., cultural informatics, bioinformatics etc). Such ontologies need to change often to reflect the latest scientific understanding on the domain at hand, and are usually associated with constraints expressed using various declarative formalisms to express domain-specific requirements, such as cardinality or acyclicity constraints. Addressing the evolution of ontologies in the presence of ontological constraints imposes extra difficulties, because it forces us to respect the associated constraints during evolution. While these issues were addressed in previous work, this is the first work to examine how ASP techniques can be applied to model and implement the evolution process. ASP was chosen for its advantages in terms of a principled, rather than ad hoc implementation, its modularity and flexibility, and for being a state-of-the-art technique to tackle hard combinatorial problems. In particular, our approach consists in providing a general translation of the problem into ASP, thereby reducing it to an instance of an ASP program that can be solved by an ASP solver. Our experiments are promising, even for large ontologies, and also show that the scalability of the approach depends on the morphology of the input.

Representing topological information for the Semantic Web often involves qualitative defined natural language terms such as "Into" or "Overlapping". This can be the case when exact coordinates of spatial regions are not available, they are incomplete or unreliable. Topological spatial relations are the most important aspect of spatial representation and reasoning, thus embedding such relations into an ontology along with their semantics as expressed using reasoning rules is an important issue. In this work we propose a representation of RCC-5 topological relations using OWL object properties and axioms, combined with reasoning rules expressed using SWRL embedded into the ontology. Three alternative representations are proposed and compared: the first is based on a straightforward implementation of the path consistency method for spatial reasoning, the second is an optimized version of the path consistency based representation implemented using an alternative representation of the topological equality relation and the third is based on the decomposition of RCC-5 relations to simpler ones. To the best of our knowledge this is the first work dealing with topological RCC-5 relations representation for the Semantic Web. In addition, our work improves the performance of topological RCC-8 relations reasoning when the best method, in terms of reasoning time, for RCC-5 relations is applied over RCC-8 relations. © 2014 ACM.

Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract

We present a module concept with algebraic interfaces and imperative implementation. It is shown that under some natural conditions, module correctness may be expressed in Hoare logic as a partial correctness assertion. Also, we discuss questions of practical verification of modules using Hoare's calculus.

Authorization is an open problem in Ambient Intelligence environments. The difficulty of implementing authorization policies lies in the open and dynamic nature of such environments. The information is distributed among various heterogeneous devices that collect, process, change, and share it. Previous work presented a fully distributed approach for reasoning with conflicts in ambient intelligence systems. This paper extends previous results to address authorization issues in distributed environments. First, the authors present the formal high-level authorization language DEAL to specify access control policies in open and dynamic distributed systems. DEAL has rich expressive power by supporting negative authorization, rule priorities, hierarchical category authorization, and nonmonotonic reasoning. The authors then define the language semantics through Defeasible Logic. Finally, they demonstrate the capabilities of DEAL in a use case Ambient Intelligence scenario regarding a hospital facility.

In this paper we elaborate on the handling of the ramification problem in the setting of temporal databases. Starting with the observation that solutions from the literature on reasoning about action are inadequate for addressing the ramification problem, in our prior work we have presented a solution based on an extension of the situation calculus and the work of McCain and Turner. In this paper, we present a tool that connects the theoretical results to practical considerations, by producing the appropriate SQL commands in order to address the ramification problem. © 2008 Springer-Verlag Berlin Heidelberg.

The imperfect nature of context in Ambient Intelligence environments and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents and provide methods for handling the imperfect and distributed nature of context. This paper proposes a solution based on the Multi-Context Systems paradigm in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To handle imperfect context, we extend Multi-Context Systems with nonmonotonic features: local defeasible theories, defeasible mapping rules, and a preference ordering on the system contexts. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve potential conflicts caused by the interaction of ambient agents through the mappings, and a distributed algorithm for query evaluation. © 2010 IEEE.

One of the current barriers towards realizing the huge potential of Future Internet is the protection of sensitive information, i.e., the ability to selectively expose (or hide) information to (from) users depending on their access privileges. Given that RDF has established itself as the de facto standard for data representation over the Web, our work focuses on controlling access to RDF data. We present a high-level access control specification language that allows fine-grained specification of access control permissions (at triple level) and formally define its semantics. We adopt an annotation-based enforcement model, where a user can explicitly associate data items with annotations specifying whether the item is accessible or not. In addition, we discuss the implementation of our framework, propose a set of dimensions that should be considered when defining a benchmark to evaluate the different access control enforcement models and present the results of our experiments conducted on different Semantic Web platforms. © 2010 Springer-Verlag Berlin Heidelberg.

The Extended Resource Description Framework has been proposed to equip RDF graphs with weak and strong negation, as well as derivation rules, increasing the expressiveness of ordinary RDF graphs. In parallel, the Modular Web framework enables collaborative and controlled reasoning in the Semantic Web. In this paper we exploit the use of the Modular Web framework to specify the modular semantics for Extended Resource Description Framework ontologies. © 2010 Springer-Verlag Berlin Heidelberg.

In this paper we describe a framework for the design of modular knowledge-based systems. The main characteristic of the framework is that verification work can be done in a local setting. We present two concrete module concepts within this framework, and give formal semantics and correctness notions for them. Finally, we show a method for proving correctness of modules using an assertional proof system for logic programs. © 1995.

Representing topological information for the Semantic Web often involves qualitative defined natural language terms such as “In”. This can be the case when exact coordinates of spatial regions are not available or they are unreliable. Topological spatial relations are the most important aspect of spatial representation and reasoning. In addition to topology, often the exact size of regions is not known as well, but the relations between sizes (e.g., larger, equal) are known. In this work we propose and evaluate an integrated representation of topological relations (RCC-5 and RCC-8) and qualitative size relations using OWL object properties and axioms, combined with reasoning rules expressed using SWRL, embedded into the ontology. Different representations are implemented and evaluated.

The imperfect nature of context in ambient intelligence environments, and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. The accomplishment of this task requires formal models that handle the involved entities as autonomous logic-based agents, and provide methods for handling the imperfect and distributed nature of context. We propose a solution based on the multi-context systems (MCS) formalism, in which local context knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules, associating concepts used by different contexts. To handle the imperfect nature of context, we extend MCS with non-monotonic features-local defeasible theories, defeasible mappings, and a preference ordering on the system contexts. In this paper, we present the novel representation model, called contextual defeasible logic, describe how its elements are used to derive distributed conclusions through a proof theory, and propose an algorithm for distributed query evaluation that implements the proof theory of contextual defeasible logic. The application of the proposed approach in a scenario from the ambient intelligence domain demonstrates how its distinct features overcome the challenges imposed by the special characteristics of ambient intelligence environments. © 2011 IEEE.

Time, as a phenomenon, has been in the focus of scientific thought from ancient times. It continues to be an important subject of research in many disciplines due to its importance as a basic aspect for understanding and formally representing change. The goal of this analytical review is to find out if the formal representations of time developed to date suffice to the needs of the basic and applied research in Computer Science, and in particular within the Artificial Intelligence and Semantic Web communities. To analyze if the existing basic theories, models, and implemented ontologies of time cower these needs well, the set of the features of time has been extracted and appropriately structured using the paper collection of the TIME Symposia series as the document corpus. This feature set further helped to structure the comparative review and analysis of the most prominent temporal theories. As a result, the selection of the subset of the features of time (the requirements for a Synthetic Theory) has been made reflecting the TIME community sentiment. Further, the temporal logics, representation languages, and ontologies available to date, have been reviewed regarding their usability aspects and the coverage of the selected temporal features. The results reveal that the reviewed ontologies of time taken together do not satisfactorily cover some important features: (i) density; (ii) relaxed linearity; (iii) scale factors; (iv) proper and periodic subintervals; (v) temporal measures and clocks. It has been concluded that a cross-disciplinary effort is required to address the features not covered by the existing ontologies of time, and also harmonize the representations addressed differently.

This paper presents DR-DEVICE, a system for defeasible reasoning on the Web. Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. In this paper we describe these scenarios in more detail along with the implementation of the DR-DEVICE system, which is capable of reasoning about RDF data over multiple Web sources using defeasible logic rules. The system is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF data that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The paper includes a use case of a semantic web broker that reasons defeasibly about renting apartments based on buyer's requirements (expressed RuleML defeasible logic rules) and seller's advertisements (expressed in RDF). © Springer-Verlag 2004.

We present a principled framework for modular Web rule bases, called MWeb. According to this framework, each predicate defined in a rule base is characterized by its defining reasoning mode, scope, and exporting rule base list. Each predicate used in a rule base is characterized by its requesting reasoning mode and importing rule base list. For legal MWeb modular rule bases S , the MWebAS and MWebWFS semantics of each rule base s ∈ S with respect to S are defined model-theoretically. These semantics extend the answer set semantics (AS) and the well-founded semantics with explicit negation (WFSX) on ELPs, respectively, keeping all of their semantical and computational characteristics. Our framework supports: (1) local semantics and different points of view, (2) local closed-world and open-world assumptions, (3) scoped negation-as-failure, (4) restricted propagation of local inconsistencies, and (5) monotonicity of reasoning, for fully shared predicates.

The operation of contraction (referring to the removal of knowledge from a knowledge base) has been extensively studied in the research field of belief change, and different postulates (e.g., the AGM postulates with recovery, or relevance) have been proposed, as well as several constructions (e.g., partial meet) that allow the definition of contraction operators satisfying said postulates. Most of the related work has focused on classical logics, i.e., logics that satisfy certain intuitive assumptions; in such logics, several nice properties and equivalences related to the above postulates and constructions have been shown to hold. Unfortunately, previous work has shown that the postulatesÊ applicability and the related results generally fail for non-classical logics. Motivated by the fact that non-classical logics (like Description Logics or Horn logic) are increasingly being used in various applications, we study contraction for all monotonic logics, classical or not. In particular, we identify several sufficient conditions for the various postulates to be applicable, and show that, in practice, relevance is a more suitable (i.e., applicable) minimality criterion than recovery for non-classical logics. In addition, we revisit some important related results from the classical belief change literature and study conditions sufficient for them to hold for non-classical logics; the corresponding results for classical logics emerge as corollaries of our more general results. Our work is another step towards the aim of exploiting the rich belief change literature for addressing the evolution problem in a larger class of logics. © 2013 Elsevier B.V.

Business rules are statements which are used to run the activities of an organization. In the era of electronic commerce it is important for these rules to be represented explicitly, and to be automatically applicable. In this paper we argue that methods from the field of knowledge representation can be used for this purpose. In particular, we propose the use of defeasible reasoning, a simple but efficient reasoning method based on rules priorities. We motivate the use of defeasible reasoning, give examples, describe two case studies, and outline current and future work in our research.

Defeasible logic is a non-monotonic formalism that deals with incomplete and conflicting information, whereas modal logic deals with the concepts of necessity and possibility. These types of logics play a significant role in the emerging Semantic Web, which enriches the available Web information with meaning, leading to better cooperation between end-users and applications. Defeasible and modal logics, in general, and, particularly, deontic logic provide means for modeling agent communities, where each agent is characterized by its cognitive profile and normative system, as well as policies, which define privacy requirements, access permissions, and individual rights. Toward this direction, this article discusses the extension of DR-DEVICE, a Semantic Web-aware defeasible reasoner, with a mechanism for expressing modal logic operators, while testing the implementation via deontic logic operators, concerned with obligations, permissions, and related concepts. The motivation behind this work is to develop a practical defeasible reasoner for the Semantic Web that takes advantage of the expressive power offered by modal logics, accompanied by the flexibility to define diverse agent behaviours. A further incentive is to study the various motivational notions of deontic logic and discuss the cognitive state of agents, as well as the interactions among them.

Representation of temporal information for the Semantic Web often involves qualitative defined information (i.e., information described using natural language terms such as “before”), since precise dates are not always available in addition to quantitative defined temporal information. This work proposes several representations for time points and intervals in ontologies by means of OWL properties and reasoning rules in SWRL embedded into the ontology. Although qualitative representations for interval and point relations exist, in addition to quantitative ones, this is the first work proposing representations combining qualitative and quantitative information for the Semantic Web. In addition to this, several existing and proposed approaches are compared using different reasoners and experimental results are presented in detail. Experimental results illustrate that reasoning performance differs greatly between different representations and reasoners. To the best of our knowledge this is the first such experimental evaluation of both qualitative and quantitative Semantic Web temporal representations.

The development of the Semantic Web proceeds in layers. Currently the most advanced layer that has reached maturity is the ontology layer, in the from of the DAML+OIL language which corresponds to a rich description logic. The next step will be the the realization of logical rule systems on top of the ontology layer. Computationally simple nonmonotonic rule systems show promise to play an important role in electronic commerce on the Semantic Web. In this paper we outline how nonmonotonic rule systems in the form of defeasible reasoning, can be built on top of description logics. © Springer-Verlag Berlin Heidelberg 2002.

The adoption of standards by the knowledge representation and logic programming communities is essential for their visibility and impact. The Rule Interchange Format is a fundamental effort in this direction that should be supported by users, developers and theoreticians. For this reason, it is essential to the community to discuss the recommendations published by the W3C RIF Working Group. In particular, this paper presents the semantics of Rule Interchange Format (RIF) of multi-documents, analyses it and some deficiencies are elicited. A more general approach is proposed as an alternative semantics for multi-documents. As a side important result, some relevant problems in the semantics of RIF-FLD are also discussed and possible ways out are proposed. © Springer-Verlag Berlin Heidelberg 2011.

Despite the great theoretical advancements in the area of Belief Revision, there has been limited success in terms of implementations. One of the hurdles in implementing revision operators is that their specification (let alone their computation), requires substantial resources. On the other hand, implementing a specific revision operator, like Dalal's operator, would be of limited use. In this paper we generalise Dalal's construction, defining a whole family of concrete revision operators, called Parametrised Difference revision operators or PD operators for short. This family is wide enough to cover a wide range of different applications, and at the same time it is easy to represent. In addition to its semantic definition, we characterise the family of PD operators axiomatically (including a characterisation specifically for Dalal's operator), we prove its' compliance with Parikh's relevance-sensitive postulate (P), we study its computational complexity, and discuss its benefits for belief revision implementations.

The Semantic Web aims to offer an interoperable environment that will allow users to safely delegate complex actions to intelligent agents. Much work has been done for agents' interoperability; especially in the areas of ontology-based metadata and rule-based reasoning. Nevertheless, the SW proof layer has been neglected so far, although it is vital for agents and humans to understand how a result came about, in order to increase the trust in the interchanged information. This paper focuses on the implementation of third party SW reasoning and proofing services wrapped as agents in a multi-agent framework. This way, agents can exchange and justify their arguments without the need to conform to a common rule paradigm. Via external reasoning and proofing services, the receiving agent can grasp the semantics of the received rule set and check the validity of the inferred results.

We consider the use of belief change techniques to address the problem of updating Knowledge Bases (KBs) based on Description Logics (DLs). We focus on the feasibility of the application of the AGM theory in DL KBs, evaluate the difficulties of the approach and determine the applicability of the method in certain families of DLs. For those DLs that are found compatible with the AGM model, we also describe a contraction operator that satisfies the AGM postulates. Finally, as an application of interest in the area of the Semantic Web, we study OWL, a W3C recommendation, and show that it is incompatible with the AGM model.

In Ambient Assisted Living and other environments the problem is to recognize all of user activities. Due to noisy or incomplete information a naïve recognition system may report activities that are logically inconsistent with each other, e.g., the user is sleeping on the couch and at the same time is watching TV. In this work, we develop a rule-based recognition system for hierarchically-organized activities that returns only logically consistent scenarios. This is achieved by explicitly formulating conflicts as Weighted Partial MaxSAT clauses to be satisfied. The system also has the ability to adjust the desired level of detail of the scenarios returned. This is accomplished by assigning preferences to clauses of the SAT problem. The system is implemented and evaluated in a real Ambient Intelligence experimental space. It is shown to be robust to the presence of noise; the level of detail can easily be adjusted by the use of two preference parameters. © 2011 Springer-Verlag.

The aim of this chapter is to give a general introduction to some of the ontology languages that play a prominent role on the Semantic Web, and to discuss the formal foundations of these languages. Web ontology languages will be the main carriers of the information that we will want to share and integrate. © Springer-Verlag Berlin Heidelberg 2005.

Domains, such as Ambient Intelligence and Social Networks, are characterized by some common features including distribution of the available knowledge, entities with different backgrounds, viewpoints and operational environments, and imperfect knowledge. Multi-Context Systems (MCS) has been proposed as a natural representation model for such environments, while recent studies have proposed adding non-monotonic features to MCS to address the issues of incomplete, uncertain and ambiguous information. In previous works, we introduced a non-monotonic extension to MCS and an argument-based reasoning model that handle imperfect context information based on defeasible argumentation. Here we propose alternative variants that integrate features such as partial preferences, ambiguity propagating and team defeat, and study the relations between the different variants in terms of conclusions being drawn in each case. © 2011 Springer-Verlag Berlin Heidelberg.

In this paper we study the brokering and matchmaking problem in the tourism domain, that is, how a requester's requirements and preferences can be matched against a set of offerings collected by a broker. The proposed solution uses the Semantic Web standard of RDF to represent the offerings, and a deductive logical language for expressing the requirements and preferences. We motivate and explain the approach we propose, and report on a prototypical implementation exhibiting the described functionality in a multi-agent environment. © Springer-Verlag Berlin Heidelberg 2005.

Ambient Intelligence environments host various agents that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of such environments and the special characteristics of ambient agents have introduced new research challenges in the study of Distributed Artificial Intelligence. This paper proposes a solution based on the Multi-Context Systems paradigm, according to which local knowledge of ambient agents is encoded in rule theories (contexts), and information flow between agents is achieved through mapping rules that associate concepts used by different contexts. To resolve potential inconsistencies that may arise from the interaction of contexts through their mappings (global conflicts), we use a preference ordering on the system contexts, which may express the confidence that an agent has in the knowledge imported by other agents. On top of this model, we have developed four alternative strategies for global conflicts resolution, which mainly differ in the type and extent of context and preference information that is used to resolve potential conflicts. The four strategies have been respectively implemented in four versions of a distributed algorithm for query evaluation and evaluated in a simulated P2P system. © 2010 Springer-Verlag London Limited.

This paper discusses some issues related to the use of rules for the Semantic Web. We argue that rule formalisms and rule-based technologies have to offer a lot for the Semantic Web. In particular, they allow a simple treatment of defeasible reasoning, which is essential for being able to capture many forms of commonsense policies and specifications. © Springer-Verlag Berlin Heidelberg 2003.

Ambient Intelligence is an emerging discipline that requires the integration of expertise from a multitude of scientific fields. The role of Artificial Intelligence is crucial not only for bringing intelligence to everyday environments, but also for providing the means for the different disciplines to collaborate. In this paper we describe the design of a reasoning framework, applied to an operational Ambient Intelligence infrastructure, that combines rule-based reasoning with reasoning about actions and causality on top of ontology-based context models. The emphasis is on identifying the limitations of the rule-based approach and the way action theories can be employed to fill the gaps. © Springer-Verlag Berlin Heidelberg 2010.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. This paper describes these scenarios in more detail, and reports on the implementation of a system for defeasible reasoning on the Web. The system (a) is syntactically compatible with RuleML; (b) features strict and defeasible rules, priorities and two kinds of negation; (c) is based on a translation to logic programming with declarative semantics; (d) is flexible and adaptable to different intuitions within defeasible reasoning; and (e) can reason with rules, RDF, RDF Schema and (parts of) OWL ontologies.

One of the current barriers towards realizing the huge poten- Tial of Future Internet is the protection of sensitive information, i.e., the ability to selectively expose (or hide) information to (from) users depend- ing on their access privileges. In this work we discuss the experiments conducted with our repository independent, portable across platforms system that supports ne-grained enforcement of RDF access control.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. This paper describes these scenarios and reports on the implementation of a system for defeasible reasoning on the Web. The system, DR-DEVICE, is capable of reasoning about RDF metadata over multiple Web sources using defeasible logic rules. It is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The paper also presents a full semantic Web broker example for apartment renting.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Such reasoning is useful in many Semantic Web applications, like policies, business rules, brokering, bargaining and agent negotiations. Nevertheless, defeasible logic is based on solid mathematical formulations and is, thus, not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions. Directed graphs can assist in confronting this drawback. They are a powerful and flexible tool of information visualization, offering a convenient and comprehensible way of representing relationships between entities. Their applicability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the arcs in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantage, by defining two distinct node types, for rules and atomic formulas, and four distinct connection types for each rule type in defeasible logic and for superiority relationships. The paper also briefly presents a tool that implements this representation methodology. © Springer-Verlag Berlin Heidelberg 2006.

Defeasible reasoning is a computationally simple nonmonotonic reasoning approach that has attracted significant theoretical and practical attention. It comprises a family of logics that capture different intuitions, among them ambiguity propagation versus ambiguity blocking, and the adoption or rejection of team defeat. This article provides a compact presentation of the defeasible logic variants, and derives an inclusion theorem which shows that different notions of provability in defeasible logic form a chain of levels of proof.

In this paper general mechanisms and syntactic restrictions are explored in order to specify and merge rule bases in the Semantic Web. Rule bases are expressed by extended logic programs having two forms of negation, namely strong (or explicit) and weak (also known as default negation or negation-as-failure). The proposed mechanisms are defined by very simple modular program transformations, and integrate both open and closed world reasoning. These program transformations are shown to be appropriate for the two major semantics for extended logic programs: answer set semantics and well-founded semantics with explicit negation. Moreover, the results obtained by both semantics are compared. © Springer-Verlag Berlin Heidelberg 2006.

The problem of modifying an ontology in response to a certain need for change is a complex and multifaceted one, being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear, as certain terms are often used with different meanings in the relevant literature. The purpose of this paper is to identify the exact relationships, connections and overlaps between these research areas and determine the boundaries of each field, by performing a broad review of the relevant literature.

The Semantic Web aims at improving the current Web, by augmenting its content with semantics and encouraging the cooperation among human users and machines. Since the basic Semantic Web infrastructure is reaching sufficient maturity, research efforts are shifting towards logic, proof and trust and rule-based systems inevitably concentrate most of the attention. Nevertheless, in order for human users to trust system answers, they have to be presented with adequate explanations that justify the derived results. And, even more importantly, these explanations have to be presented in a user-comprehensible format. Consequently, the focus in this work is on humans and the research area called proof visualization that features three main approaches: tree-based, graphical and logical/textual. Since each of the approaches presents advantages and disadvantages, this article proposes a fourth, hybrid visualization approach that combines the pros of all three approaches and attempts to leverage the respective cons. The article also presents a software tool that implements the proposed hybrid approach. The tool is called VProofH and visualizes defeasible logic proofs, offering multiple representations that adapt to user needs. Extensive scalability and user evaluation tests prove the software tool's usability. © 2010 Elsevier B.V. All rights reserved.

The study of different variants of default logic reveales not only differences but also properties they share. For example, there seems to be a close relationship between semi-monotonicity and the guaranteed existence of extensions. Likewise, formula-manipulating default logics tend to violate the property of cumulativity. The problem is that currently such properties must be established separately for each approach. This paper describes some steps towards the study of properties of classes of default logics by giving a rather general definition of what a default logic is. Essentially our approach is operational and restricts attention to purely formula-manipulating logics. We motivate our definition and demonstrate that it includes a variety of well-known default logics. Furthermore, we derive general results regarding the concepts of semimonotonicity and cumulativity. As a benefit of the discussion we uncover that some design decisions of concrete default logics were not accidental as they may seem, but rather they were due to objective necessities.

The ubiquitous intelligence vision requires the development of systems that integrate sensing, computing and networking with advanced techniques for data and knowledge representation and management. Taking advantage of these opportunities, this paper presents a context-aware navigation guide, strongly connected to the semantics of user profile. Our approach, which focuses on indoor environments, uses OWL ontologies to capture and formally model context information, and reasons on the ontology data using rules in order to support personalized contextaware navigation services. To test and demonstrate the proposed approach, a prototype has been developed that documents the flexibility of the design. © 2009 IEEE.

One of the drawbacks of Reiter's original presentation of Default Logic is the difficulty of expressing priorities among defaults. Recently, Brewka proposed an expansion of Default Logic which allows for explicit reasoning about priorities in Default Logic. In this paper we give an alternative, operational characterization of extensions; it is also technically simpler and therefore more easily applicable. Further we investigate some properties of the logic paying special attention to the existence of extensions. We present some sufficient conditions for a default theory with priorities to have at least one extension.

Previous works have shown that the AGM theory cannot be used as the basis for defining contraction operators for several ontology representation languages. In this paper, we examine the postulate of relevance which has been proposed in the belief revision literature as a more intuitive alternative to the AGM postulate of recovery. Even though relevance and recovery have been proven to be equivalent in the presence of the other AGM postulates in classical logics, we show that this is not true for non-classical ones. Based on this fact, we are able to show that the relevance postulate is a very attractive alternative to recovery for ontology evolution, as it can be used to define contraction operators in all interesting ontology representation languages.

We give an introduction to default logic, one of the most prominent nonmonotonic logics. Emphasis is given to providing an operational interpretation for the semantics of default logic that is usually defined by fixed-point concepts (extensions). We introduce a process model that allows to exactly calculate the extensions of a default theory in a quite easy way. We give a prototypical implementation of processes in Prolog able to handle the examples that can be found in literature. Finally, we develop some theoretical results about default logic and give new simple proofs using the process model as a theoretical tool. © 1994 Kluwer Academic Publishers.

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts based on base concepts. For example, rules allow to define the extension of a class or property based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on partial logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes. © Springer-Verlag Berlin Heidelberg 2005.

This paper descirbes Exten, an object-oriented system for default reasoning. Its current functionality includes the computation of extensions for various default logics. The efficiency of the system is strongly increased by applying pruning techniques to the search tree. We motivate and present these techniques, and demonstrate that they can cut down the size of the search tree significantly. Quite importantly, they complement very well the recently developed stratification method [4] which has proven to be powerful and has been implemented in our system. Exten supports experimentation with default logics allowing the user to set various parameters. Also it has been designed to be open to future enhancements, which are supported by its object-oriented design. Exten is part of our long-term effort to develop an integrated toolkit for intelligent information management based on nonmonotonic reasoning and belief revision methods.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web. However, the RuleML syntax of defeasible logic may appear too complex for many users. Furthermore, the interplay between various technologies and languages, such as defeasible reasoning, RuleML, and RDF impose a demand for using multiple, diverse tools for building rule-based applications for the Semantic Web. In this paper we present VDR-Device, a visual integrated development environment for developing and using defeasible logic rule bases on top of RDF ontologies. VDR-Device integrates in a user-friendly graphical shell, a visual RuleML-compliant rule editor that constrains the allowed vocabulary through analysis of the input RDF ontologies and a defeasible reasoning system that processes RDF data and RDF Schema ontologies. © Springer-Verlag Berlin Heidelberg 2005.

The language of dl-programs is a latest effort in developing an expressive representation for Web-based ontologies. It allows to build answer set programming (ASP) on top of description logic and thus some attractive features of ASP can be employed in the design of the Semantic Web architecture. In this paper we first generalize dl-programs by allowing multiple knowledge bases and then accordingly, define the answer set semantics for the dl-programs. A novel technique called forgetting is developed in the setting of dl-programs and applied to ontology merging and aligning. © Springer-Verlag Berlin Heidelberg 2005.

Web ontology languages will be the main carriers of the information that we will want to share and integrate. The aim of this chapter is to give a general introduction to some of the ontology languages that play a prominent role on the Semantic Web. In particular, it will explain the role of ontologies on the Web and in ICT, review the current standards of RDFS and OWL, and discuss open issues for further developments.

This paper discusses the approach taken by the Rule Markup Language (RuleML) Initiative towards a general Web rule language framework and relates it to the MDA and UML by the Object Management Group (OMG). It also presents the abstract syntax of RuleML 0.85 as a MOF/UML model and considers the possibility to integrate RuleML with OCL and Action Semantics. © 2004 IEEE.

In this paper we discuss some issues related to the intuitions of defeasible reasoning. Defeasible logic serves as the formal basis for our analysis. We also make some comments on the comparison between defeasible logics and the well-founded semantics of extended logic programs with priorities.

Ontologies and automated reasoning are the building blocks of the Semantic Web initiative. Derivation rules can be included in an ontology to define derived concepts, based on base concepts. For example, rules allow to define the extension of a class or property, based on a complex relation between the extensions of the same or other classes and properties. On the other hand, the inclusion of negative information both in the form of negation-as-failure and explicit negative information is also needed to enable various forms of reasoning. In this paper, we extend RDF graphs with weak and strong negation, as well as derivation rules. The ERDF stable model semantics of the extended framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive feature of our theory, which is based on Partial Logic, is that both truth and falsity extensions of properties and classes are considered, allowing for truth value gaps. Our framework supports both closed-world and open-world reasoning through the explicit representation of the particular closed-world assumptions and the ERDF ontological categories of total properties and total classes.

Multi-Context Systems (MCS) are logical formalizations of distributed context theories connected through a set of mapping rules, which enable information flow between different contexts. Reasoning in MCS introduces many challenges that arise from the heterogeneity of contexts with respect to the language and inference system that they use, and from the potential conflicts that may arise from the interaction of context theories through the mappings. This study proposes a P2P rule-based reasoning model for MCS, which handles (a) incomplete or inconsistent local context information, by representing contexts as local theories of Defeasible Logic and performing local defeasible reasoning, and (b) global inconsistencies that result from the integration of local contexts, by representing mappings as defeasible rules and performing some type of distributed defeasible reasoning. It also provides a distributed algorithm for query evaluation, analyzes its formal properties, and illustrates its use in a Semantic Web use case scenario. © 2008 Springer Berlin Heidelberg.

The study of ambient computing environments and pervasive computing systems has introduced new research challenges in the field of Distributed Artificial Intelligence. The imperfect nature of context, the different viewpoints from which the ambient agents face the available context, and their heterogeneity with respect to the language and inference system that they use cannot be efficiently handled by the classical centralized reasoning approaches followed by most of the systems presented so far. The current paper proposes a distributed reasoning approach from the field of Multi-Context Systems (MCS) that handles these requirements by modeling ambient agents as peers in a P2P system, local context knowledge as local rule theories, and mapping rules through which an ambient agent imports context knowledge from other ambient agents as defeasible rules. To resolve potential inconsistencies that may derive from the interaction of context theories through the mappings, it uses a preference relation, which may express the trust that an agent has in the knowledge imported by other ambient agents. The paper also describes a specific distributed algorithm for query evaluation in the proposed MCS framework, analyzes its formal properties, and demonstrates its use in three use case scenarios from the Ambient Intelligence domain. © 2008 Springer Berlin Heidelberg.

Location-based services have evolved significantly during the last few years and are reaching a maturity phase, relying primarily on the experience gained and the utilization of recent technologies. Taking advantage of these opportunities, this paper presents a context-aware navigation guide, strongly connected to the semantics behind user profile. Our approach, which focuses on indoor environments, uses OWL ontologies to capture and formally model profile and context information, and reasons on the ontology data using rules in order to support personalized context-aware navigation services. To test and demonstrate the approach, a prototype has been developed that documents the flexibility of the design. © 2008 Springer Berlin Heidelberg.

Reasoning in Ambient Computing environments requires formal models that represent ambient agents as autonomous logic-based entities, and support sharing and distributed reasoning with the available ambiguous context information. This paper presents an approach from the field of Multi-Context Systems that handles these requirements by modeling contexts as local rule theories in a P2P system and mappings through which the agents exchange context information as defeasible rules, and by performing some type of distributed defeasible reasoning. © 2008 Springer Berlin Heidelberg.

Recently there has been increased interest in logic programming-based default reasoning approaches which are not using negation-as-failure in their object language. Instead, default reasoning is modelled by rules and a priority relation among them. Historically the first logic in this class was Defeasible Logic. In this paper we will study its relationship to other approaches which also rely on the idea of using logic rules and priorities. In particular we will study sceptical LPwNF, courteous logic programs, and priority logic.

We present a principled framework for modular web rule bases, called MWeb. According to this framework, each predicate defined in a rule base is characterized by its defining reasoning mode, scope, and exporting rule base list. Each predicate used in a rule base is characterized by its requesting reasoning mode and importing rule base list. For valid MWeb modular rule bases S, the MWebAS and MWebWFS semantics of each rule base s ε S w.r.t. S are defined, model-theoretically. These semantics extend the answer set semantics (AS) and the well-founded semantics with explicit negation (WFSX) on ELPs, respectively, keeping all of their semantical and computational characteristics. Our framework supports: (i) local semantics and different points of view, (ii) local closed-world and open-world assumptions, (iii) scoped negation-as-failure, (iv) restricted propagation of local inconsistencies, and (v) monotonicity of reasoning, for "fully shared" predicates. Copyright © 2008, Association for the Advancement of Artificial Intelligence.

Abstract

Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with ontologies is the modification of an existing ontology in response to a certain need for change. This problem is a complex and multifaceted one, because it can take several different forms and includes several related subproblems, like heterogeneity resolution or keeping track of ontology versions. As a result, it is being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear, as the same term is often used with different meanings in the relevant literature, creating a certain amount of confusion. The purpose of this paper is to identify the exact relationships between these research areas and to determine the boundaries of each field, by performing a broad review of the relevant literature.

ERDF stable model semantics is a recently proposed semantics for ERDF ontologies and a faithful extension of RDFS semantics on RDF graphs. In this paper, we elaborate on the computability and complexity issues of the ERDF stable model semantics. We show that decidability under this semantics cannot be achieved, unless ERDF ontologies of restricted syntax are considered. Therefore, we propose a slightly modified semantics for ERDF ontologies, called ERDF #n-stable model semantics. We show that entailment under this semantics is in general decidable and it also extends RDFS entailment. An equivalence statement between the two semantics and various complexity results are provided. © 2008 Springer Berlin Heidelberg.

In this work we present the design and implementation of a multi-agent environment for serving proof explanations in the Semantic Web. The system allows users or agents to issue queries, on a given RDF& rules knowledge base and automatically produces proof explanations for answers produced by a popular programming system (JENA), by interpreting the output from the proof's trace and converting it into a meaningful representation. It also supports an XML representation (a R2ML language extension) for agent communication, which is a common scenario in the Semantic Web. The system in essence implements a proof layer for rules on the Semantic Web empowering trust between agents and users. © 2008 Springer-Verlag Berlin Heidelberg.

The notion of uniform interpolation for description logic ALC has been introduced in [9]. In this paper, we reformulate the uniform interpolation for ALC from the angle of forgetting and show that it satisfies all desired properties of forgetting. Then we introduce an algorithm for computing the result of forgetting in concept descriptions. We present a detailed proof for the correctness of our algorithm using the Tableau for ALC. Our results have been used to compute forgetting for ALC knowledge bases. © Springer-Verlag Berlin Heidelberg 2009.

Location-based services have evolved significantly during the last few years and are reaching a maturity phase, relying primarily on the experience gained and the utilization of recent technologies, such as ontology-based modeling and rule-based reasoning. Ontologies are used to classify the terms used in a particular application, characterize possible relationships, and define possible constraints on using those relationships. Thus, they provide a suitable means for representing context models. The formal semantics of ontology-based approaches also enable simple reasoning tasks on the context information. Rule-based reasoning techniques are also used in order to offer extensive reasoning capabilities. Taking advantage of these opportunities, this paper presents C-NGINE, a Contextual Navigation Guide for Indoor Environments, strongly connected to the semantics behind user profile. Our approach, which focuses on indoor environments, uses OWL ontologies to capture and formally model profile and context information, and reasons on the ontology data using rules in order to support personalized context-aware navigation services. To test and demonstrate the approach, a prototype has been developed that documents the flexibility of the design. © 2009 IOS Press and the authors. All rights reserved.

Location-based ubiquitous services have evolved significantly during the last years and are reaching a maturity phase, relying primarily on the experience gained and the utilisation of recent technologies. This paper presents a context-aware platform for mobile devices in dynamic environments, which uses Semantic Web technologies to model context information and advanced interactive map-based interfaces for accommodating pedestrians. To demonstrate the approach, a prototype has been developed and a number of further extensions are analysed. Building and using this system has enabled us to identify the main challenges that need to be addressed for realising the objectives of next generation semantics-based pervasive information systems. © 2009 Inderscience Enterprises Ltd.

In this work, we present the design and implementation of a system for proof explanation in the Semantic Web, based on defeasible reasoning. Trust is a vital feature for Semantic Web. If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs. Our system produces automatically proof explanations using a popular logic programming system (XSB), by interpreting the output from the proof's trace and converting it into a meaningful representation. It also supports an XML representation for agent communication, which is a common scenario in the Semantic Web. In this paper, we present the design and implementation of the system, a RuleML language extension for the representation of a proof explanation, and we give some examples of the system. The system in essence implements a proof layer for nonmonotonic rules on the Semantic Web. © 2007 Elsevier B.V. All rights reserved.

SQL, Prolog, RDF and OWL are among the most prominent and most widely used computational logic languages. However, SQL, Prolog and RDF do not allow the representation of negative information, only OWL does so. RDF does not even include any negation concept. While SQL and Prolog only support reasoning with closed predicates based on negation-as-failure, OWL supports reasoning with open predicates based on classical negation, only. However, in many practical application contexts, one rather needs support for reasoning with both open and closed predicates. To support this claim, we show that the well-known Web vocabulary FOAF includes all three kinds of predicates i.e. closed, open and partial predicates. Therefore, reasoning with FOAF data, as a typical example of reasoning on the Web, requires a formalism that supports the distinction between open and closed predicates. We argue that ERDF, an extension of RDF, offers a solution to deal with this problem. © Copyright 2008 front matter by the editors.

Integrating context-aware notification services to ubiquitous computing systems aims at the provision of the right information to the right users, at the right time, in the right place, and on the right device, and constitutes a significant step towards the realization of the Ambient Intelligence vision. In this paper, we present FlexConf, a semantics-based system that supports location-based, personalized notification services for the assistance of conference attendees. Its special features include an ontology-based representation model, rule-based context-aware reasoning, and a novel positioning system for indoor environments. © Springer-Verlag 2009.

Defeasible reasoning is a well-established nonmonotonic reasoning approach that has recently been combined with semantic web technologies. This paper describes modal and deontic extensions of defeasible logic, and shows how these extensions can bbe used for modelling multi-agent systems and policies. Copyright 2008 ACM.

This paper reports on a system for automated agent negotiation, based on a formal and executable approach to capture the behavior of parties involved in a negotiation. It uses the JADE agent framework, and its major distinctive feature is the use of declarative negotiation strategies. The negotiation strategies are expressed in a declarative rules language, defeasible logic, and are applied using the implemented system DR-DEVICE. The key ideas and the overall system architecture are described, and a particular negotiation case is presented in detail. © 2007 Elsevier B.V. All rights reserved.

This paper reports on a system for automated agent negotiation. The negotiation strategies are expressed in defeasible logic, and are applied using the implemented reasoning system DR-DEVICE. The overall system architecture is described, and a particular 1-1 negotiation scenario is presented in detail. © 2004 Springer-Verlag Berlin/Heidelberg.

Defeasible logic is a nonmonotonic reasoning approach based on rules and priorities. Its design supports efficient implementation, and it shows promise to be deployed successfully in applications. So far, only static priorities have been used, provided by an external superiority relation. In this article we show how dynamic priorities can be integrated, where priority information is obtained from the deductive process itself. Dynamic priorities have been studied for other related reasoning systems such as default logic and argumentation. We define a proof theory, study its formal properties, and provide an argumentation semantics. © 2004 Wiley Periodicals, Inc.

Publisher Default logic is an important method of knowledge representation and reasoning, because it supports reasoning with incomplete information, and because defaults can be found naturally in many application domains, such as diagnostic problems, information retrieval, legal reasoning, regulations, specifications of systems and software, etc. Default logic can be used either to model reasoning with incomplete information that was the original motivation or as formalism that enables compact representation of information. This chapter discusses the basic concepts and ideas of default logic, and the presentation was based on the operational interpretations, rather than on fixpoints. The operational interpretations allow learners to apply concepts to concrete problems in a straightforward way. This is an important point because the difficulty of understanding default logic should not be underestimated. In some cases, standard default logic is insufficient to resolve conflicts among defaults. Preferences provide a declarative way to solve this problem and thus many approaches to preference handling in default logic are proposed.

In this paper we study the brokering and matchmaking problem, that is, how a requester's requirements and preferences can be matched against a set of offerings collected by a broker. The proposed solution uses the Semantic Web standard of RDF to represent the offerings, and a deductive logical language for expressing the requirements and preferences. We motivate and explain the approach and architecture we propose, and report on a system implementing the described functionality in a multi-agent environment. Finally, we experimentally evaluate the core reasoning engine of the system. © 2006 Elsevier B.V. All rights reserved.

Location-based ubiquitous services have evolved significantly during the last years and are reaching a maturity phase, relying primarily on the experience gained and the utilization of recent technologies. This paper proposes a context-aware platform for mobile devices in dynamic environments, which uses Semantic Web technologies to model context information and advanced interactive map-based interfaces for accommodating pedestrians. To test and demonstrate the approach, a prototype has been developed and a number of further extensions are studied. Building and using this system has enabled us to identify the main challenges that need to be addressed for realizing the objectives of next generation semantics-based pervasive information systems. © Springer-Verlag Berlin Heidelberg 2007.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is, among others, useful for ontology integration, where conflicting information arises naturally; and for the modeling of business rules and policies, where rules with exceptions are often used. This paper describes these scenarios in more detail, and reports on the implementation of a system for defeasible reasoning on the Web. The system (a) is syntactically compatible with RuleML; (b) features strict and defeasible rules and priorities; (c) is based on a translation to logic programming with declarative semantics; and (d) is flexible and adaptable to different intuitions within defeasible reasoning. © 2004 IEEE.

Recently there has been increased interest in logic programming-based default reasoning approaches which are not using negation-as-failure in their object language. Instead, default reasoning is modelled by rules and a priority relation among them. In this paper we compare the expressive power of two approaches in this family of logics. Defeasible Logic, and sceptical Logic Programming without Negation as Failure (LPwNF). Our results show that the former has a strictly stronger expressive power. The difference is caused by the latter logic's failure to capture the idea of teams of rules supporting a specific conclusion.

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumption that fluents persist, and because actions have effects on the next situation only. In this paper we provide a solution to the ramification problem based on an extension of the situation calculus and the work of McCain and Turner. More specifically, we study the case where the effects of an action refer to the past, a particularly complex problem. © 2005 Elsevier B.V. All rights reserved.

In this article, we discuss some issues related to the intuitions of defeasible reasoning, in particular floating conclusions, reinstatement, and zombie paths. Defeasible logic serves as the formal basis for our analysis. We also make some comments on the comparison between defeasible logics and the well-founded semantics of extended logic programs with priorities. © 2006 Wiley Periodicals, Inc.

Huge amounts of data are generated by sensor readings, social media and databases. Such data introduce new challenges due to their volume and variety, and thus, new techniques are required for their utilization. We believe that reasoning can facilitate the extraction of new and useful knowledge. In particular, we may apply reasoning in order to make and support decisions, clean noisy data and derive high-level information from low-level input data. In this work we discuss the problem of large-scale reasoning over incomplete or inconsistent information, with an emphasis on nonmonotonic reasoning. We outline previous work, challenges and possible solutions, both over MapReduce and alternative high performance computing infrastructures.

Nonmonotonic rule systems are expected to play an important role in the layered development of the Semantic Web. Defeasible reasoning is a direction in nonmonotonic reasoning that is based on the use of rules that may be defeated by other rules. It is a simple, but often more efficient approach than other nonmonotonic rule systems for reasoning with incomplete and inconsistent information. This paper reports on the implementation of a system for defeasible reasoning on the Web. The system 1) is syntactically compatible with RuleML, 2) features strict and defeasible rules, priorities, and two kinds of negation, 3) is based on a translation to logic programming with declarative semantics, 4) is flexible and adaptable to different intuitions within defeasible reasoning, and 5) can reason with rules, RDF, RDF Schema, and (parts of) OWL ontologies. © 2007 IEEE.

Electronic Brokering is a good candidate for taking up Semantic Web technology. In this paper we study the brokering and matchmaking problem that is, how a requester's requirements and preferences can be matched against a set of offerings collected by a broker. The proposed solution uses the Semantic Web standard of RDF to represent the offerings, and a deductive logical language, based on non-monotonic reasoning, for expressing the requirements and preferences. We motivate and explain the approach we propose, and report on a prototypical implementation exhibiting the described functionality, in JADE agent environment. © 2005 IEEE.

It is generally acknowledged that any Knowledge Base (KB) should be able to adapt itself to new information received. This problem has been extensively studied in the field of belief change, the dominating approach being the AGM theory. This theory set the standard for determining the rationality of a given belief change mechanism but was placed in a certain context which makes it inapplicable to logics used in the Semantic Web, such as Description Logics (DLs) and OWL. We believe the Semantic Web community would benefit from the application of the AGM theory to such logics. This paper is a preliminary study towards the feasibility of this application. Our approach raises interesting theoretical challenges and has an important practical impact too, given the central role that DLs and OWL play in the Semantic Web. © Springer-Verlag Berlin Heidelberg 2005.

The concept of forgetting has received significant interest in artificial intelligence recently. Informally, given a knowledge base, we may wish to forget about (or discard) some redundant parts (such as atoms, predicates, concepts, etc) but still preserve the consequences for certain forms of reasoning. In nonmonotonic reasoning, so far forgetting has been studied only in the context of extension based approaches, mainly answer-set programming. In this paper forgetting is studied in the context of defeasible logic, which is a simple, efficient and sceptical nonmonotonic reasoning approach. © 2012 Springer-Verlag.

The Resource Description Framework (RDF) has become the defacto standard for representing information in the Semantic Web. Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. In this paper we advocate the use of an abstract access control model to ensure the selective exposure of RDF information. The model is defined by a set of abstract operators and tokens. Tokens are used to label RDF triples with access information. Abstract operators model RDF Schema inference rules and propagation of labels along the RDF Schema (RDFS) class and property hierarchies. In this way, the access label of a triple is a complex expression that involves the labels of the triples and the operators applied to obtain said label. Different applications can then adopt different concrete access policies that encode an assignment of the abstract tokens and operators to concrete (specific) values. Following this approach, changes in the interpretation of abstract tokens and operators can be easily implemented resulting in a very flexible mechanism that allows one to easily experiment with different concrete access policies (defined per context or user). To demonstrate the feasibility of the approach, we implemented our ideas on top of the MonetDB and Post-greSQL open source database systems. We conducted an initial set of experiments which showed that the overhead for using abstract expressions is roughly linear to the number of triples considered; performance is also affected by the characteristics of the dataset, such as the size and depth of class and property hierarchies as well as the considered concrete policy. Copyright 2012 ACM.

There are ongoing efforts to provide declarative formalisms of integrity constraints over RDF/S data. In this context, addressing the evolution of RDF/S knowledge bases while respecting associated constraints is a challenging issue, yet to receive a formal treatment. We provide a theoretical framework for dealing with both schema and data change requests. We define the notion of a rational change operator as one that satisfies the belief revision principles of Success, Validity and Minimal Change. The semantics of such an operator are subject to customization, by tuning the properties that a rational change should adhere to. We prove some interesting theoretical results and propose a general-purpose algorithm for implementing rational change operators in knowledge bases with integrity constraints, which allows us to handle uniformly any possible change request in a provably rational and consistent manner. Then, we apply our framework to a well-studied RDF/S variant, for which we suggest a specific notion of minimality. For efficiency purposes, we also describe specialized versions of the general evolution algorithm for the RDF/S case, which provably have the same semantics as the general-purpose one for a limited set of (useful in practice) types of change requests. © 2012 Springer-Verlag London Limited.

One of the crucial tasks towards the realization of the Semantic Web vision is the efficient encoding of human knowledge in ontologies. Thus, the proper maintenance of these, usually large, structures and, in particular, their adaptation to new knowledge (ontology evolution) is one of the most challenging problems in the current Semantic Web research. In this paper, we uncover a certain gap in the current research area of ontology evolution and propose a research direction based on belief revision. We present some results in this direction and argue that our approach introduces an interesting new dimension to the problem that is likely to find important applications in the future. © Springer-Verlag Berlin Heidelberg 2006.

The importance of integrating rules and ontologies for the Semantic Web has been well addressed by many researchers. Defeasible Logic is a simple but efficient nonmonotonic language which can handle both defeasibility and priority. In this paper we propose a novel approach to combining Defeasible Logic with Description Logics by introducing the Description Defeasible Logic (DDL). DDL is similar to Defeasible Logic but it also contains queries to the Description Logic knowledge base. DDL allows nonmonotonic reasoning to be built on top of ontologies, and to a certain degree, allows ontologies to be built on top of nonmonotonic reasoning. We give some basic properties of DDL, one of which shows that DDL is a tractable language provided that the underlying Description Logic is tractable. © 2004 Springer-Verlag Berlin/Heidelberg.

Understanding why and how a given answer to a query is generated from a deductive or relational database is fundamental to obtain justifications, assess trust, and detect dependencies on contradictions. Propagating provenance information is a major technique that evolved in the database literature to address the problem, using annotated relations with values from a semiring. The case of positive programs/relational algebra is well-understood but handling negation (or set difference in relational algebra) has not been addressed in its full generality or has deficiencies. The approach defined in this work provides full provenance information for logic programs under the least model, well-founded semantics and answer set semantics, and is related to the major existing notions of justifications for all these logic programming semantics. © 2013 Springer-Verlag.

Abstract

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

Defeasible logic is a non-monotonic formalism that deals with incomplete and conflicting information, whereas modal logic deals with the concepts of necessity and possibility. These types of logics play a significant role in the emerging Semantic Web, which enriches the available Web information with meaning, leading to better cooperation between end-users and applications. Defeasible and modal logics, in general, and, particularly, deontic logic provide means for modeling agent communities, where each agent is characterized by its cognitive profile and normative system, as well as policies, which define privacy requirements, access permissions, and individual rights. Toward this direction, this article discusses the extension of DR-DEVICE, a Semantic Web-aware defeasible reasoner, with a mechanism for expressing modal logic operators, while testing the implementation via deontic logic operators, concerned with obligations, permissions, and related concepts. The motivation behind this work is to develop a practical defeasible reasoner for the Semantic Web that takes advantage of the expressive power offered by modal logics, accompanied by the flexibility to define diverse agent behaviours. A further incentive is to study the various motivational notions of deontic logic and discuss the cognitive state of agents, as well as the interactions among them.

The proofs of theorems in default logic are often quite complicated and therefore error-prone. In this paper we introduce an operational interpretation of default logic and show its usefulness in providing technically simpler proofs for well-known results. We especially turn our attention to ordered, semi-normal default theories. We point out that Etherington's result and most of the citations and improvements in the literature are wrong, give the correct version and a totally new, technically clearer and more comprehensive proof. © 1994 J.C. Baltzer AG, Science Publishers.

The provenance (i.e., origins) of derived information on the Web is crucial in many applications to allow information quality assessment, trust judgments, accountability, as well as understanding the temporal and spatial status of the information. On the other hand, the inclusion of negative information in knowledge representation both in the form of negation-as-failure and explicit negation is also important to allow various forms of reasoning, provided that weakly negated information is associated with the sources (contexts) in which it holds. In this work, we consider collections of g-RDF ontologies, distributed over the web, along with a set of conflict statements expressing that information within a pair of g-RDF ontologies cannot be combined together for deriving new information. A g-RDF ontology is the combination of (i) a g-RDF graph G (i.e., a set of positive and strongly negated RDF triples, called g-RDF triples) and (ii) a g-RDF program P containing derivation rules with possibly both explicit and scoped weak negation. Information can be inferred through the g-RDF graphs or the derivation rules of the g-RDF ontologies, or through the RDFS derivation rules. We associate each derived grounded g-RDF triple [¬] p(s, o) with the set of names S of the g-RDF ontologies that contributed to its derivation. To achieve this, we define the provenance stable models of a g-RDF ontology collection. We show that our provenance g-RDF semantics faithfully extends RDFS semantics. Finally, we provide an algorithm based on Answer Set Programming that computes all provenance stable models of a g-RDF ontology collection and provides the answer to various kinds of queries. Various complexity results are provided. © 2013 Springer Science+Business Media Dordrecht.

Anomalies such as redundant, contradictory or deficient knowledge in a knowledge base indicate possible errors. Various methods for detecting such anomalies have been introduced, analyzed and applied in the past years, but they usually deal with rule-based systems. So far, little attention has been payed to the verification and validation of nonmonotonic knowledge bases, although there are good reasons to expect that such knowledge bases will be increasingly used in practical applications. This paper discusses how classical verification methods may be applied to detect some anomalies in nonmonotonic knowledge bases. These anomalies are first described in a formal way, and then generic verification methods to detect them are presented.

The development of the Semantic Web proceeds in steps, building each layer on top of the other. Currently, the focus of research efforts is concentrated on logic and proofs, both of which are essential, since they will allow systems to infer new knowledge by applying principles on the existing data and explain their actions. Research is shifting towards the study of non-monotonic systems that are capable of handling conflicts among rules and reasoning with partial information. As for the proof layer of the Semantic Web, it can play a vital role in increasing the reliability of Semantic Web systems, since it will be possible to provide explanations and/or justifications of the derived answers. This paper reports on the implementation of a system for visualizing proof explanations on the Semantic Web. The proposed system applies defeasible logic, a member of the non-monotonic logics family, as the underlying inference system. The proof representation schema is based on a graph-based methodology for visualizing defeasible logic rule bases. © 2008 Springer Berlin Heidelberg.

Evolving out of theoretical and practical work, the paper presents the motivation and basic ideas for the construction and use of modular knowledge bases. The approach relates to earlier work carried out by each of the two authors of the paper separately. A model is introduced that merges the two previous approaches, modules for logical knowledge bases, and ordering by generality domains, while maintaining their benefits. Central aims are reusability, the restriction of memory searching, and the management of inconsistent (competing) knowledge within one knowledge base. The model is explained using examples, and the formal semantics are discussed of structured, modular knowledge bases for knowledge representations that are based on logic programming. © 1994.

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and inconsistent information. Such reasoning is useful for many applications in the Semantic Web, such as policies and business rules, agent brokering and negotiation, ontology and knowledge merging, etc. However, the syntax of defeasible logic may appear too complex for many users. In this paper we present a graphical authoring tool for defeasible logic rules that acts as a shell for the DR-DEVICE defeasible reasoning system over RDF metadata. The tool helps users to develop a rule base using the OO-RuleML syntax of DR-DEVICE rules, by constraining the allowed vocabulary through analysis of the input RDF namespaces, so that the user does not have to type-in class and property names. Rule visualization follows the tree model of RuleML. The DR-DEVICE reasoning system is implemented on top of the CLIPS production rule system and builds upon an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules. © Springer-Verlag Berlin Heidelberg 2005.

Trust is a vital feature for the Semantic Web: If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs, and this issue is the topic of the proof layer in the design of the Semantic Web. This paper presents the design of a system for proof explanation on the Semantic Web, based on defeasible reasoning. The basis of this work is the DR-DEVICE system that is extended to handle proofs. A critical aspect is the representation of proofs in an XML language, which is achieved by a RuleML language extension. © Springer-Verlag Berlin Heidelberg 2007.

Research in many fields of AI, such as distributed planning and reasoning, agent teamwork and coalition formation, cooperative problem solving and action theory has advanced significantly over the last years, both from a theoretical and a practical perspective. In the light of the development towards ambient, pervasive and ubiquitous computing, this research will be tested under new, more demanding realistic conditions, stimulating the emergence of novel approaches to handle the challenges that these open, dynamic environments introduce. This paper identifies shortcomings of state-of-the-art techniques in handling the complexity of the Ambient Intelligence vision, motivated by the experience gained during the development and usage of a contextaware platform for mobile devices in dynamic environments. The paper raises research issues and discusses promising directions for realizing the objectives of near-future pervasive information systems. © Springer-Verlag Berlin Heidelberg 2007.

Default logic is a useful formalism for reasoning with incomplete information. Exten is a system for computing default logic using stratification to increase efficiency. It is capable of computing first-order Reiter, justified, and constrained default extensions. Stratification allows the computation of extensions in a modular way, giving the user flexibility in editing default theories and changing various parameters.

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learning model and a knowledge-based model) for assisting adult ADHD diagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future.

Adult referrals to specialist autism spectrum disorder diagnostic services have increased in recent years, placing strain on existing services and illustrating the need for the development of a reliable screening tool, in order to identify and prioritize patients most likely to receive an ASD diagnosis. In this work a detailed overview of existing approaches is presented and a data driven analysis using machine learning is applied on a dataset of adult autism cases consisting of 192 cases. Our results show initial promise, achieving total positive rate (i.e., correctly classified instances to all instances ratio) up to 88.5%, but also point to limitations of currently available data, opening up avenues for further research. The main direction of this research is the development of a novel autism screening tool for adults (ASTA) also introduced in this work and preliminary results indicate the ASTA is suitable for use as a screening tool for adult populations in clinical settings.

As Large Language Models gain prominence in the AI landscape, it is essential to understand their capabilities and limitations, among others in terms of reasoning. This paper is a first step towards understanding the capabilities in terms of defeasible rule-based reasoning. It presents results of initial experiments and discussed future research directions.

We argue that modularity is essential for the design, verification, and maintenance of large-scale knowledge based systems. Motivated by work on software modules and algebraic specification, we introduce a module concept with formal interfaces, and give semantics and correctness notions for such modules based on logic programming. Single modules communicate with their environment by their interfaces. We discuss how modular systems can be built from single modules by means of so-called module operations, and derive for the composition operation results concerning compositionality of semantics and correctness preservation.

Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.

We present a method of representing some classes of default theories as normal logic programs. The main point is that the standard semantics (i.e. SLDNF-resolution) computes answer substitutions that correspond exactly to the extensions of the represented default theory. We explain the steps of constructing a logic program LogProg(P,D) from a given default theory (P,D), and present the proof ideas of the soundness and completeness results for the approach.

Default reasoning is one of the most common forms of nonmonotonic reasoning, and is found in many application domains. Default logic and its variants have been proposed as knowledge representation methods capable of default reasoning. One problem of all these methods is that their concepts are given in terms of fixed-point equations, thus making them difficult to understand and use. In this paper, we present simple, constructive approaches for the semantics of some default logic variants, and show their correctness.

The aim of this paper is to describe the algorithmic foundations of the part of the program Exten responsible for the computation of extensions in Constrained Default Logic. Exten is a system that computes extensions for various default logics. The efficiency of the system is increased by pruning techniques for the search tree. We motivate and present these techniques, and demonstrate that they can cut down the size of the search tree significantly. Quite importantly, they complement well the recently developed stratification method. This technique has to be modified to work properly with Constrained Default Logic, and we show how this can be done. Exten supports experimentation with default logic, allowing the user to set various parameters. Also it has been designed to be open to future enhancements, which are supported by its object-oriented design. Exten is part of our long-term effort to develop an integrated toolkit for intelligent information management based on nonmonotonic reasoning and befief revision methods.

This is an overview paper on default logic and its variants. Default reasoning is one of the most prominent approaches to nonmonotonic reasoning, and allows one to make plausible conjectures when faced with incomplete information about the problem at hand. Default rules prevail in many application domains such as medical diagnosis and leagal reasoning. Default logic in its original form suffers from some deficiencies, and several variants have been developed in the past years

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Knowledge representation and reasoning in the legal domain has primarily focused on case studies where knowledge and data can fit in main memory. However, this assumption no longer applies in the era of big data, where large amounts of data are generated daily. This paper discusses new opportunities and challenges that emerge in relation to reasoning with legal big data and the concepts of volume, velocity, variety and veracity. A four-layer legal big data framework is proposed to manage the complete lifecycle of legal big data from sourcing, processing and storage, to reasoning, analysis and consumption. Within each layer, a number of relevant future research directions are also identified, which can facilitate the realisation of knowledge-rich legal big data solutions.

Attention-Deficit Hyperactivity Disorder (ADHD) is a mental condition characterised by a pattern of inattention, hyperactivity, and/or impulsivity that causes significant impairment across various domains. Delayed diagnosis and treatment for ADHD can be harmful to people, leading to broader mental health conditions. This paper presents a fully functional system for diagnosing ADHD using an Artificial Intelligence (AI) system called Neuro Intel. Positive results from our research has led to the development of Neuro Intel, which incorporates both expert clinician knowledge and historical clinical data using Machine Learning to assist clinicians in the diagnosis of ADHD in adults.

Purpose Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition that affects human behaviour and can interfere with daily activities and relationships. Medication or medical information about ADHD can be found in several data sources on the Web. Such distribution of knowledge raises notable obstacles since researchers and clinicians must manually combine various sources to deeply explore aspects of ADHD. Knowledge graphs have been widely used in medical applications due to their data integration capabilities, offering rich data stores of information built from heterogeneous sources; however, general purpose knowledge graphs cannot represent knowledge in sufficient detail, thus there is an increasing interest in domain-specific knowledge graphs. Methods In this work we propose a Knowledge Graph of ADHD. In particular, we introduce an automated procedure enabling the construction of a knowledge graph that covers knowledge from a wide range of data sources primarily focusing on adult ADHD. These include relevant literature and clinical trials, prescribed medication and their known side-effects. Data integration between these data sources is accomplished by employing a suite of information linking procedures, which aim to connect resources by relating them to common concepts found in medical thesauri. Results The usability and appropriateness of the developed knowledge graph is evaluated through a series of use cases that illustrate its ability to enhance and accelerate information retrieval. Conclusion The Knowledge Graph of ADHD can provide valuable assistance to researchers and clinicians in the research, training, diagnostic and treatment processes for ADHD.

Most information systems are faced with incomplete information, even for simple database applications; therefore, they must make plausible conjectures in order to operate in a satisfactory way. A simple example is the closed world assumption, which is used extensively in the database area. Nonmonotonic reasoning (NMR) provides formal methods which support such a behavior; in default logic, for example, the plausible conjectures are based on "rules of thumb." Information is subject to change due to the inherent uncertainty of information or because the environment is volatile and dynamic. Current nonmonotonic reasoning systems neglect the problems raised by change. Belief revision (BR) is the research area that has developed techniques capable of dealing with changing information. This paper presents the motivations, the design decisions, and the current state of the CIN Project (Changing Information), whose aim is to provide an integrated toolkit of nonmonotonic reasoning and belief revision methods. The reason we design an open system is our contention that finding the right method for NMR and BR is an elusive dream, and that we should instead seek to determine the most appropriate method for the specific problem at hand. © Elsevier Science Inc. 1997.

Default reasoning is computationally expensive. One of the most promising ways of easing this problem and developing powerful implementations is to split a default theory into smaller parts and compute extensions in a modular, `local' way. Up to now this idea was only followed for Reiter's default logic, yet it is also relevant to other variants of default logic which have found increasing recognition in the past years. This paper shows how it can be modified to work for several popular, alternative approaches of default reasoning: Justified, Constrained and Rational Default Logic. This work defines the formal basis for a Web-based default reasoning system which is under development at our institution.

Default logic is computationally expensive. One of the most promising ways of easing this problem and developing powerful implementations is to split a default theory into smaller parts and compute extensions in a modular, 'local' way. This paper compares two recent approaches, Turner's splitting and Cholewinski's stratification. It shows that the approaches are closely related - in fact the former can be viewed as a special case of the latter.

A production system (PS) is a forward chaining rule-based system used to build large expert systems. Testing a PS must involve the construction of a covering set of test data but it is not clear what the meaning of covering a PS is and how a test data set can be measured according to coverage. We propose a test data coverage measure for a subset for PS with well defined semantics. We use a correspondence between PS and function free first order Horn logic programs to define the declarative coverage notion and measure. We found that the coverage measure can be used to determine the coverage of the program logic of a PS as well as to automate test data generation. Unification theory is utilised to measure test data coverage and constrained inductive generation is used for test data construction.

Anomalies such as redundant, contradictory, or deficient knowledge in a knowledge base indicate possible errors. Various methods for detecting such anomalies have been introduced, analyzed, and applied in the past years, but they usually deal with rule-based systems. So far, little attention has been paid to the verification and validation of more complex representations, such as nonmonotonic knowledge bases, although there are good reasons to expect that these technologies will be increasingly used in practical applications. This article does a step towards the verification of knowledge bases which include defaults by providing a theoretical foundation of correctness concepts and a classification of possible anomalies. It also points out how existing verification methods may be applied to detect some anomalies in nonmonotonic knowledge bases, and discusses methods of avoiding potential inconsistencies (in the context of default reasoning inconsistency means nonexistence of extensions).

Over the past years, two main approaches to computational intelligence have emerged: the symbolic and the non-symbolic approach. The perhaps most prominent methods of the symbolic approach are based on logic . Logical methods exhibit a series of desirable properties:

[bull ] Transparent representation of meaning

[bull ] Precise understanding of the meaning of statements (semantics).

[bull ] Sound reasoning methods.

[bull ] Explanation capabilities.

A special session on logical methods for computational intelligence was held at the 3rd Joint Conference on Information Sciences . The field of computational logic is so broad that it is impossible to review the main developments in an article. Therefore, in the following we will restrict attention to two areas that turned out to be the focus of the special session: automated reasoning, and reasoning with incomplete and changing information.

Ontologies have emerged as one of the key issues in information integration and interoperability and in their application to knowledge management and electronic commerce. A trend towards formal methods for ontology management is obvious. This paper discusses a concept which can be expected to bc of great importance to formal ontology management, and which is well-known in traditional software development: refinement. We define and discuss ontology refinement, give illustrating examples, and highlight its advantages as compared to other forms of ontology revision.

Conservative extensions of logical theories play an important role in software engineering. They provide a formal basis for program refinement and guarantee the integrity and transparency of modules and objects. This paper provides a detailed analysis of conservative extension concepts in the context of nonmonotonic knowledge bases, in particular default theories. Since there are different approaches to nonmonotonic reasoning based on different strategies for dealing with multiple extensions, we define several alternative refinement concepts and study their interrelationships. We also show that refinement is well behaved with respect to strong stratification, a technique for reducing computational effort in default reasoning.

Defeasible logic is a simple but efficient rule-based non-monotonic logic. It has powerful implementations and shows promise to be applied in the areas of legal reasoning and the modelling of business rules. So far defeasible logic has been defined only proof-theoretically. Argumentation-based semantics have become popular in the area of logic programming. In this paper we give an argumentation-based semantics for defeasible logic. Recently it has been shown that a family of approaches can be built around defeasible logic, in which different intuitions can be followed. In this paper we present an argumentation-based semantics for an ambiguity propagating logic, too. Further defeasible logics can be characterised in a similar way. © Springer-Verlag Berlin Heidelberg 2000.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we will compare an ambiguity propagating defeasible logic with default logic. In fact the two logics take rather contrary approaches: defeasible logic takes a directly deductive approach, whereas default logic is based on alternative possible world views, called extensions. Computational complexity results suggest that default logics are more expressive than defeasible logics. This paper answers the opposite direction: an ambiguity propagating defeasible logic can be directly embedded into default logic.

Regulations are a wide-spread and important part of government and business. They codify how products must be made and processes should be performed. Such regulations can be difficult to understand and apply. In an environment of growing complexity of, and change in, regulation, automated support for reasoning with regulations is becoming increasingly necessary. In this paper we claim that such automated support can be provided on the basis of defeasible logical rules. We highlight the support that can be provided by this logical tool, and illustrate some aspects using examples from one specific domain: university regulations.

The stratification of default theories has been studied to increase the efficiency of default reasoning. The idea is to split the knowledge into smaller parts, and to apply reasoning in a local way. This paper shows how stratification can work for some important variants of Default Logic. These variants include Justified Default Logic, Rational Default Logic, and Constrained Default Logic.

ERDF stable model semantics is a recently proposed semantics for ERDF ontologies and a faithful extension of RDFS semantics on RDF graphs. In this paper, we elaborate on the computability and complexity issues of the ERDF stable model semantics. Based on the undecidability result of ERDF stable model semantics, decidability under this semantics cannot be achieved, unless ERDF ontologies of restricted syntax are considered. Therefore, we propose a slightly modified semantics for ERDF ontologies, called ERDF #n-stable model semantics. We show that entailment under this semantics is, in general, decidable and also extends RDFS entailment. Equivalence statements between the two semantics are provided. Additionally, we provide algorithms that compute the ERDF #n-stable models of syntax-restricted and general ERDF ontologies. Further, we provide complexity results for the ERDF #n-stable model semantics on syntax-restricted and general ERDF ontologies. Finally, we provide complexity results for the ERDF stable model semantics on syntax-restricted ERDF ontologies.

Academia and industry are investigating novel approaches for processing vast amounts of data coming from enterprises, the Web, social media and sensor readings in an area that has come to be known as Big Data. Logic programming has traditionally focused on complex knowledge structures/programs. The question arises whether and how it can be applied in the context of Big Data. In this paper, we study how the well-founded semantics can be computed over huge amounts of data using mass parallelization. Specifically, we propose and evaluate a parallel approach based on the X10 programming language. Our experiments demonstrate that our approach has the ability to process up to 1 billion facts within minutes.

Scientific research activities generate a large amount of data, which varies in format, volume, structure and ownership. Although there are revision control systems and databases developed for data archiving, the traditional data management methods are not suitable for High-Performance Computing (HPC) systems. The files in such systems do not have semantic annotations and cannot be archived and managed for public dissemination. We have proposed and developed a Research Data Management (RDM) system, ‘iCurate’, which provides easy-to-use RDM facilities with semantic annotations. The system incorporates Metadata Retrieval, Departmental Archiving,Workflow Management System, Meta data Validation and Self Inferencing. The ‘i’ emphasises the user-oriented design. iCurate will support researchers by annotating their data in a clearer and machine readable way from its production to publication for the future reuse.

argumentation framework (AF) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to AFs are missing, thus limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup.

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However, these datasets often contain errors which limit their value to medical research, with one study finding error rates ranging from 2.3%-26.9% in a selection of medical databases. Previous methods for automatically assessing data quality normally rely on threshold rules, which are often unable to correctly identify errors, as further complex domain knowledge is required. To combat this, a semantic web based framework has previously been developed to assess the quality of medical data. However, early work, based solely on traditional semantic web technologies, revealed they are either unable or inefficient at scaling to the vast volumes of medical data. In this paper we present a new method for storing and querying medical RDF datasets using Hadoop Map / Reduce. This approach exploits the inherent parallelism found within RDF datasets and queries, allowing us to scale with both dataset and system size. Unlike previous solutions, this framework uses highly optimised (SPARQL) joining strategies, intelligent data caching and the use of a super-query to enable the completion of eight distinct SPARQL lookups, comprising over eighty distinct joins, in only two Map / Reduce iterations. Results are presented comparing both the Jena and a previous Hadoop implementation demonstrating the superior performance of the new methodology. The new method is shown to be five times faster than Jena and twice as fast as the previous approach.

This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance, proving the effectiveness of the approach in real world setups.

Normal forms play an important role in computer science, for example in the areas of logic and databases. This paper provides a study of normal forms for some prominent logics for default reasoning. In particular we show that in Constrained and in Justified Default Logic, semi-normal default theories can represent arbitrary default theories. The main result for Justified Default Logic requires the signature (logical language to be enhanced in order to obtain the desired outcome.

Default reasoning is concerned with making inferences in cases where the information at hand is incomplete. In such cases it is necessary to make plausible assumptions, which in default reasoning are based on default rules. This paper gives an introduction to the field. It discusses in depth one particular approach, default logic, including properties, semantics and computational models. It also gives an overview of other ideas and approaches.

Default reasoning comprises methods of reasoning with uncertain and incomplete information which share the idea of using default rules to represent plausible conclusions. Even though the applicational scope of default reasoning was clear right from the beginning, research focussed heavily on theoretical aspects and neglected for a long time pragmatic and applicational questions. This, coupled with a presentation that is usually too technical for potential applicants of this technology, has led to the impression that default reasoning (and other forms of nonmonotonic reasoning) have very little relevance to practice. This paper seeks to counter this view. It describes the basic advantages of using defaults in the representation of information and in reasoning with information. And then it presents applications of default reasoning in various areas, such as software engineering, information retrieval, law, graphics design etc.

Cumulativity is an important property of nonmonotonic inference relations because it allows for the safe use of lemmas. This note shows that Justified Default Logic, Lukaszewicz variant of default logic, is cumulative for prerequisite-free default theories, a question that has been open in the literature. The proof is very simple and makes use of an operational interpretation of extensions. © 1998 Taylor & Francis Group, LLC.

The notion of a conservative extension and its derivatives: refinement and abstraction, in the context of ontologies is discussed. Any information system uses its own ontology, either implicitly or explicitly. It is shown that refinement allows for local changes in information integration, as opposed to other forms of ontology revision. The graph-based approach may also facilitate the development of efficient algorithms to test whether an ontology is a refinement of another or to find the least common refinement of two ontologies.

We are witnessing an explosion of available data from the Web, government authorities, scientific databases, sensors and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application - or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling the vast amounts of data for these applications. In this paper, we consider nonmonotonic reasoning, which has traditionally focused on rich knowledge structures. In particular, we consider defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge data sets. Our experimental results demonstrate that defeasible reasoning with billions of data is performant, and has the potential to scale to trillions of facts. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Ambient Intelligence environments consist of various devices that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of ambient environments, and the special characteristics of the involved devices have introduced new research challenges on how to represent and reason with contextual information. Previous work presented a solution based on an extension of Multi-Context Systems through the use of defeasible reasoning to reason efficiently with conflicts. This paper reports on initial experiences gained from the deployment of contextual defeasible reasoning in real environments. We report on the architecture of an implementation on small devices, present the definition and implementation of two concrete application scenarios, and discuss the performance and issues of scalability of the approach. © 2010 IEEE.

In this paper, we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumption that fluents persist, and because actions have effects on the next situation only. In this paper, we provide a solution to the ramification problem based on an extension of the situation calculus and the work ofMcCain and Turner.More specifically, we study the case in which two or more actions execute concurrently, a particularly complex problem. © 2010 Wiley Periodicals, Inc.

The success of the Semantic Web is impossible without any form of modularity, encapsulation, and access control. In an earlier paper, we extended RDF graphs with weak and strong negation, as well as derivation rules. The ERDF #n-stable model semantics of the extended RDF framework (ERDF) is defined, extending RDF(S) semantics. In this paper, we propose a framework for modular ERDF ontologies, called modular ERDF framework, which enables collaborative reasoning over a set of ERDF ontologies, while support for hidden knowledge is also provided. In particular, the modular ERDF stable model semantics of modular ERDF ontologies is defined, extending the ERDF #n-stable model semantics. Our proposed framework supports local semantics and different points of view, local closed-world and open-world assumptions, and scoped negation-as-failure. Several complexity results are provided. © 2009 Springer-Verlag Berlin Heidelberg.

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumption that fluents persist, and because actions have effects on the next situation only. In this paper we provide a solution to the ramification problem based on an extension of the situation calculus and the work of McCain and Turner. We present a tool with a graphical user interface for addressing the ramification problem in temporal databases.

In this work we present " AlertMe" , a semanticsbased, context-aware notification system that provides personalized alerts to graduate students based on their preferences. An extensive description of the system is carried out. We present the underlying ontology that models the available knowledge, as well as how higher level knowledge inference and context-based decision making is achieved through rule-based reasoning. Finally, we outline the technical aspects of the developed system, covering issues involving the integration of the various subcomponents. © 2009 IEEE.

The preservation of digital objects is a topic of prominent importance for archives and digital libraries. This paper focuses on the problem of preserving the performability of tasks on digital objects. It formalizes the problem in terms of Horn Rules and details the required inference services. The proposed framework and methodology is more expressive and flexible than previous attempts as it allows expressing the various properties of dependencies (e.g. transitivity, symmetry) straightforwardly. Finally, the paper describes how the proposed approach can be implemented using various technologies. © Springer-Verlag Berlin Heidelberg 2010.

Multi-Context Systems (MCS) are logical formalizations of distributed context theories connected through a set of mapping rules, which enable information flow between different contexts. Recent studies have proposed adding non-monotonic features to MCS to handle problems such as incomplete, uncertain or ambiguous context information. In previous work, we proposed a non-monotonic extension to MCS and an argument-based reasoning model that enable handling cases of imperfect context information based on defeasible reasoning. To deal with ambiguities that may arise from the interaction of context theories through mappings, we used a preference relation, which is represented as a total ordering on the system contexts. Here, we extend this approach to additionally deal with incomplete preference information. To enable this, we replace total preference ordering with partial ordering, and modify our argumentation framework and the distributed algorithms that we previously proposed to meet the new requirements. © 2010 Springer-Verlag.

In this paper we study the semantic consistency maintenance issue between heterogeneous contexts, that is, how an inquiry from an unknown user of an e-marketplace can be received and answered in a semantically consistent way by a firm that is not in the context of the user's e- marketplace. The proposed solution uses XPM to represent semantically consistent business concepts and adopts defeasible logic to reason with XPM document-oriented business rules for inquiring and offering. We motivate the approach with a real-world apartment rental problem, and explain it in architecture of collaborative business process design and automatic service provision. Finally, we report on an implementation specification within a hybrid human- agent framework. © 2008 IEEE.

The algorithms dealing with the incorporation of new knowledge in an ontology (ontology evolution) often share a rather standard process of dealing with changes. This process consists of the specification of the language, the determination of the allowed update operations, the identification of the invalidities that could be caused by each such operation, the determination of the various alternatives to deal with each such invalidity, and, finally, some selection mechanism for singling out the "best" of these alternatives. Unfortunately, most ontology evolution algorithms implement these steps using a case-based, ad-hoc methodology, which is cumbersome and error-prone. The first goal of this paper is to present, justify and make explicit the five steps of the process. The second goal is to propose a general framework for ontology change management that captures this process, in effect generalizing the methodology employed by existing tools. The introduction of this framework allows us to devise a whole class of ontology evolution algorithms, which, due to their formal underpinnings, avoid many of the problems exhibited by ad-hoc frameworks. We exploit this framework by implementing a specific ontology evolution algorithm for RDF ontologies as part of the FORTH-ICS Semantic Web Knowledge Middleware (SWKM). © 2008 Springer-Verlag Berlin Heidelberg.

As the program co-chairs for 2009 IEEE International Conference on e-Business Engineering, it is our pleasure to welcome you to Macau, China. We hope you enjoy the technical program that we have put together for you and that you enjoy your interactions with the wonderful group of researchers and practitioners who have come together for this great event.

The imperfect nature of context in Ambient Intelligence environments and the special characteristics of the entities that possess and share the available context information render contextual reasoning a very challenging task. Most current Ambient Intelligence systems have not successfully addressed these challenges, as they rely on simplifying assumptions, such as perfect knowledge of context, centralized context, and unbounded computational and communicating capabilities. This paper presents a knowledge representation model based on the Multi-Context Systems paradigm, which represents ambient agents as autonomous logic-based entities that exchange context information through mappings, and uses preference information to express their confidence in the imported knowledge. On top of this model, we have developed an argumentation framework that exploits context and preference information to resolve conflicts caused by the interaction of ambient agents through mappings, and a distributed algorithm for query evaluation. © 2009 Springer Berlin Heidelberg.

Defeasible reasoning is a simple but efficient approach to nonmonotonic reasoning that has recently attracted considerable interest and that has found various applications. Defeasible logic and its variants are an important family of defeasible reasoning methods. So far no relationship has been established between defeasible logic and mainstream nonmonotonic reasoning approaches. In this paper we establish close links to known semantics of extended logic programs. In particular, we give a translation of a defeasible theory D into a program P(D).We showtha t under a condition of decisiveness, the defeasible consequences of D correspond exactly to the sceptical conclusions of P(D) under the stable model semantics. Without decisiveness, the result holds only in one direction (all defeasible consequences of D are included in all stable models of P(D)). If we wish a complete embedding for the general case, we need to use the Kunen semantics of P(D), instead. © Springer-Verlag Berlin Heidelberg 2002.

In this work we present the design and general architecture of DR-Prolog, a system for defeasible reasoning and proof explanation in the Semantic Web, and the implementation of three different tools that constitute the DR-Prolog Tool Suite: (a) the DR-Prolog API; (b) the DR-Prolog Web application; and (c) the DR-Prolog desktop application. DR-Prolog supports reasoning with Defeasible Logic theories and ontological knowledge in RDF(S) and OWL, is compatible with RuleML, and enables extracting meaningful proof explanations for the answers it computes. © 2008 Springer-Verlag Berlin Heidelberg.

Conservative extensions of (classical) logical theories play an important role in software engineering, because they provide a formal basis for program refinement and guarantee the integrity and transparency of modules and objects. Similarly specification morphisms play a central role for information hiding and combining modules. Surprisingly, while the use of nonmonotonic theories for describing knowledge systems which may contain incomplete or uncertain data has been advocated for some time now, the above concepts have yet to be applied in this area. The aim of this work is to develop and apply analogues of these concepts in a nonmonotonic context. This paper builds on previous results, which focus on conservative extensions, extending the ideas to the more general case of specification morphisms.

The study of different variants of default logic reveals not only differences but also properties they share. For example, there seems to be a close relationship between semi-monotonicity and the guaranteed existence of extensions. Likewise, formula-manipulating default logics tend to violate the property of cumulativity. The problem is that currently such properties must be established separately for each approach. This paper describes some steps towards the study of properties of classes of default logics by giving a rather general definition of what a default logic is. Essentially our approach is operational and restricts attention to purely formula-manipulating logics. We motivate our definition and demonstrate that it includes a variety of well-known default logics. Furthermore, we derive general results regarding the concepts of semi-monotonicity and cumulativity. As a benefit of the discussion we uncover that some design decisions of concrete default logics were not accidental as they may seem, but rather they were due to objective necessities. © 1998 Taylor & Francis Group, LLC.

Context, Context Representation and Contextual Reasoning constitute central notions in the Ambient Intelligence vision to transform our living and working environments into 'intelligent spaces'. Ontology-based models have been argued to satisfy all demands concerning context representation. Rule-based reasoning has already been successfully integrated in ontology-based applications for domains with similar requirements (e.g. the Web), while it offers significant advantages concerning its deployment in the Ambient Intelligence domain. In this paper, we analyze the general challenges of contextual reasoning, argue about the suitability of rule-based reasoning, and describe the deployment of such methods in two different settings; in a centralized semantics-based context management framework for Ambient Intelligence, and in a totally distributed system of logic-based abient agents. © 2010 Springer-Verlag.

This paper discusses the significance of nonmonotonic reasoning, a method from the knowledge representation area, to mainstream software engineering. In particular, we discuss why the use of defaults in specifications is an adequate way of addressing some of the most important problems in requirements engineering, such as: The problem of identifying and dealing with inconsistencies; evolving system requirements; requirements prioritization; and the quality of specifications with respect to naturalness and compactness. We argue that these problems need to be addressed in a principled, formal way, and that default reasoning provides adequate mechanisms to deal with them.

Multi-Context Systems are logical formalizations of distributed context theories connected through mapping rules, which enable information flow between different contexts. Reasoning in Multi-Context Systems introduces many challenges that arise from the heterogeneity of contexts with regard to the language and inference system that they use, and from the potential conflicts that may arise from the interaction of context theories through the mappings. The current paper proposes four alternative strategies for using context and preference information to resolve conflicts in a Multi-Context Framework, in which contexts are modeled as rule theories, mappings as defeasible rules, and global inconsistency is handled using methods of distributed defeasible reasoning. © 2009 International Federation for Information Processing.

Default logic is a prominent rigorous method of reasoning with incomplete information based on assumptions. It is a static reasoning approach, in the sense that it doesn't reason about changes and their consequences. On the other hand, its nonmonotonic behaviour appears when a change to a default theory is made. This paper studies the dynamic behaviour of default logic in the face of changes, a concept that we motivate by a reference to requirements engineering. The paper defines a contraction and a revision operator, and studies their properties. This work is part of an ongoing project whose aim is to build an integrated, domain-independent toolkit of logical methods for reasoning with changing and incomplete information. The techniques described in this paper will be implemented as part of the toolkit.

Defeasible reasoning is a well-established nonmonotonic reasoning approach that has recently been combined with Semantic Web technologies. This paper describes modal and deontic extensions of defeasible logic, motivated by potential applications for modelling multi-agent systems and policies. It describes a logic metaprogram that captures the underlying intuitions, and outlines an implemented system. Finally, it demonstrates its use for modelling policies. © 2008 Elsevier Ltd. All rights reserved.

Default reasoning is computationally expensive. One of the most promising ways of easing this problem and developing powerful implementations is to split a default theory into smaller parts and compute extensions in a modular, "local" way. Up to now this idea was only followed for Reiter's default logic, yet it is also relevant to other variants of default logic which have found increasing recognition in the past years. This paper shows how it can be modified to work for several popular, alternative approaches of default reasoning: justified, constrained and rational default logic. This work defines the formal basis for a Web-based default reasoning system which is under development at our institution. © 1998 John Wiley & Sons, Inc.

Forgetting is an important tool for reducing ontologies by eliminating some concepts and roles while preserving sound and complete reasoning. Attempts have previously been made to address the problem of forgetting in relatively simple description logics (DLs) such as DL-Lite and extended εL. The ontologies used in these attempts were mostly restricted to TBoxes rather than general knowledge bases (KBs). However, the issue of forgetting for general KBs in more expressive description logics, such as ALC and OWL DL, is largely unexplored. In particular, the problem of characterizing and computing forgetting for such logics is still open. In this paper, we first define semantic forgetting about concepts and roles in ALC ontologies and state several important properties of forgetting in this setting. We then define the result of forgetting for concept descriptions in ALC , state the properties of forgetting for concept descriptions, and present algorithms for computing the result of forgetting for concept descriptions. Unlike the case of DL-Lite, the result of forgetting for an ALC ontology does not exist in general, even for the special case of concept forgetting. This makes the problem of how to compute forgetting in ALC more challenging. We address this problem by defining a series of approximations to the result of forgetting for ALC ontologies and studying their properties and their application to reasoning tasks. We use the algorithms for computing forgetting for concept descriptions to compute these approximations. Our algorithms for computing approximations can be embedded into an ontology editor to enhance its ability to manage and reason in (large) ontologies. © Springer-Verlag Berlin Heidelberg 2009.

The Ambient Intelligence paradigm has evolved significantly during the last few years and aims at developing context-aware and adaptive systems that enable people to use personalized services. Contextawareness, semantics enriched services and adaptivity are among the features that characterize Ambient Intelligence systems. In this paper, we describe an ontology-based user model that captures and formally describes profile and context information, and enables rule-based reasoning on the available data to support personalized context-aware navigation services. We also describe how the proposed model is used as a basic building block for the development of a contextual guide for users in indoor environments, C-NGINE [1] (Contextual Navigation Guide for INdoor Environments). © 2008 IEEE.

We present a method of representing some classes of default theories as normal logic programs. The main point is that the standart semantics (i.e., SLDNF-resolution) computes answer substitutions that correspond exactly to the extensions of the represented default theory. This means that we give a correct implementation of default logic. We explain the steps of constructing a logic program LogProg(P, D) from a given default theory (P, D), give some examples, and derive soundness and completeness results.

The subject of nonmonotonic reasoning is reasoning with incomplete information. One of the main approaches is autoepistemic logic in which reasoning is based on introspection. This paper aims at providing a smooth introduction to this logic, stressing its motivation and basic concepts. The meaning (semantics) of autoepistemic logic is given in terms of so-called expansions which are usually defined as solutions of a fixed-point equation. The present paper shows a more understandable, operational method for determining expansions. By improving applicability of the basic concepts to concrete examples, we hope to make a contribution to a wider usage of autoepistemic logic in practical applications.

Defeasible logic is a non-monotonic formalism that deals with incomplete and conflicting information. Modal logic deals with necessity and possibility, exhibiting defeasibility; thus, it is possible to combine defeasible logic with modal operators. This paper reports on the extension of the DR-DEVICE defeasible reasoner with modal and deontic logic operators. The aim is a practical defeasible reasoner that will take advantage of the expressiveness of modal logics and the flexibility to define diverse agent types and behaviors. © 2008 IEEE.

Logic is currently the target of the majority of the upcoming efforts towards the realization of the Semantic Web vision, namely making the content of the Web accessible not only to humans, as it is today, but to machines as well. Defeasible reasoning, a rule-based approach to reasoning with incomplete and conflicting information, is a powerful tool in many Semantic Web applications. Despite its strong mathematical background, logic, in general, and defeasible logic, in particular, may overload the user with tons of additional complex semantic relationships among data and metadata of the Semantic Web. To this end, a comprehensible, visual representation of these semantic relationships (rules) would help users understand them and make more use of them. This paper presents VDR-DEVICE, a defeasible reasoning system, designed specifically for the Semantic Web environment. VDR-DEVICE is an integrated development environment for deploying and visualizing defeasible logic rule bases on top of RDF Schema ontologies. The system consists of a number of sub-components, which, though developed autonomously, are combined efficiently, forming a flexible framework. The system employs a defeasible reasoning system that supports direct importing and processing of RDF data and RDF Schema ontologies as well as a number of user-friendly rule base and ontology visualization modules. © 2008 Elsevier B.V. All rights reserved.

Defeasible logic is a logic-programming based nonmonotonic reasoning formalism which has an efficient implementation. It makes use of facts, strict rules, defeasible rules, defeaters, and a superiority relation. We clarify the proof theory of defeasible logic through an analysis of the conclusions it can draw. Using it, we show that defeaters do not add to the expressiveness of defeasible logic, among other results. The analysis also supports the restriction of defeasible logic to admit only acyclic superiority relations.

Default logic is computationally expensive. One of the most promising ways of easing this problem and developing powerful implementations is to split a default theory into smaller parts and compute extensions in a modular, "local" way. This paper compares two recent approaches, Turner's splitting and Cholewinski's stratification. It shows that the approaches are closely related - in fact the former can be viewed as a special case of the latter. © 1999 Kluwer Academic Publishers.

The revision and transformation of knowledge is widely recognized as a key issue in knowledge representation and reasoning. Reasons for the importance of this topic are the fact that intelligent systems are gradually developed and refined, and that often the environment of an intelligent system is not static but changes over time. Traditionally belief revision has been concerned with revising first order theories. Nonmonotonic reasoning provides rigorous techniques for reasoning with incomplete information. Until recently the dynamics of nonmonotonic reasoning approaches has attracted little attention. This paper studies the dynamics of defeasible logic, a simple and efficient form of nonmonotonic reasoning based on defeasible rules and priorities. We define revision and contraction operators and propose postulates. Our postulates try to follow the ideas of AGM belief revision as far as possible, but some AGM postulates clearly contradict the nonmonotonic nature of defeasible logic, as we explain. Finally we verify that the operators satisfy the postulates.

Default logic is a prominent rigorous method for reasoning with incomplete information based on assumptions. It is a static reasoning approach, in the sense that it doesn’t reason about changes and their consequences. On the other hand, its nonmonotonic behaviour appears when changes to a default theory are made. This paper studies the dynamic behaviour of default logic in the face of changes. We consider the operations of contraction and revision, present several solutions to these problems, and study their properties.

Large Language Models (LLMs) have been the focus of Artificial Intelligence (AI) research recently but evaluation of their performance demonstrated their limitations in various tasks requiring reasoning capabilities. Responses of LLMs often contain erroneous answers and non existent facts, which is a major problem especially in critical tasks such as medical applications. In this work we propose a solution to this problem by making use of Linked Open Data as a source of reliable information. Specifically, we propose an approach that leverages Large Language Models (LLM) in order to allow for automatic SPARQL query generation from natural language by providing example entries of the dataset to the LLM so that it can analyze its structure. Preliminary results demonstrate the potential of our approach, and we provide an online demo so that users can experiment.

Background: Attention-Deficit/Hyperctivity Disorder (ADHD) is characterized by core symptoms of inattention, hyperactivity, and impulsivity that fluctuate dynamically based on context. Standard diagnostic criteria provide static descriptions, failing to capture this variability, while existing computational models may lack interpretability or flexibility for clinical application. There is a need for dynamic, theory-driven models to represent ADHD. Objective: This study aimed to develop and present a set of interpretable mathematical models representing the dynamic, context-dependent nature of the core symptoms of ADHD, grounded in established neuropsychological principles. Methods: Algebraic equations were formulated to represent symptom dynamics. Inattention was modelled using modulated exponential decay functions. Hyperactivity was represented by a modulated sinusoidal function reflecting its oscillatory pattern. Impulsive choice was modelled using hyperbolic delay discounting combined with a probabilistic softmax choice rule. Results: The study produced specific mathematical equations that quantify the temporal dynamics and contextual modulation for each core symptom domain. These equations provide a formal representation of how attention decays, hyperactivity fluctuates, and impulsive choices are made, incorporating individual sensitivities and situational factors pertinent to ADHD. Conclusion: The proposed mathematical models offer a novel, quantitative framework for understanding and representing the dynamic nature of ADHD symptoms. Grounded in neuropsychological theory, these interpretable models provide a potential advance over static descriptions and may facilitate improved clinical assessment, personalized treatment strategies, and targeted research into the mechanisms underlying ADHD. Further empirical validation is warranted to establish their clinical utility. Further empirical validation is warranted to establish their clinical utility.

The rapid advancement of emerging technologies in the big data era has led to an exponential increase in the size of streaming data (a.k.a data streams) char acterised by the 3Vs: volume, velocity, and variety. Beyond the computational scalability required for processing and managing streaming data, anomaly detec tion poses additional complexities, particularly in the absence of prior knowledge or contextual information about these data streams. In this paper, a novel and scalable anomaly detection method, namely big data Stream Contextual-based AnomaLy dEtection (SCALE) is proposed based on distributed Directed Acyclic Graph (DAG) topology, leveraging data flow Model of Computation (MoC) to aggregate, process, and managing data streams in real-time. We proposed two new algorithms to filter anomaly events and compute temporal context attributes in window partitions. Finally, our comprehensive experimental results demon strate that SCALE achieves high throughput, high computational detection accuracy, and low processing latenc

Introduction: Attention Deficit Hyperactivity Disorder (ADHD) in adults remains challenging to diagnose accurately, with over- and under-diagnosis common due to reliance on subjective clinical judgement. Although machine learning (ML) tools have shown promise in improving diagnostic accuracy, their limited transparency restricts clinical adoption. Existing research rarely integrates broad clinical, substance-use, and quality-of-life measures into a unified predictive framework, nor does it systematically compare explainable artificial intelligence (XAI) outputs with traditional statistical analyses. Methods: We retrospectively analysed 786 anonymised adult assessments (January 2019–December 2024) from a UK specialist mental health service. The dataset included demographics; validated symptom scales (MDQ, GAD-7, PHQ-9, CAARS, DIVA); substance-use screens (AUDIT, DAST); and EQ-5D-3L quality-of-life indices. An XGBoost classifier was trained using a stratified split and evaluated on the held-out test set. Model interpretability was examined using SHapley Additive exPlanations (SHAP). SHAP attributions were triangulated with traditional exploratory analyses, including Pearson correlation matrices and Welch’s t-tests, to validate feature relevance and identify interaction effects. Results: The model achieved 77% accuracy and an AUC-ROC of 0.82. CAARS ADHD Raw scores and DIVA adulthood inattentiveness emerged as the strongest predictors of ADHD diagnosis. SHAP analysis revealed important interaction patterns, including depressive symptom severity (PHQ-9) amplifying the predictive contribution of ADHD symptom scales. Age and gender moderated key feature effects, suggesting demographic variability in symptom expression. Traditional EDA confirmed the statistical significance of these predictors while highlighting complementary linear associations, supporting the robustness of the SHAP-derived explanation profiles. Discussion: Integrating multimodal clinical features with transparent ML methods provides interpretable, clinically aligned insights into adult ADHD diagnosis. The combined SHAP–EDA approach identifies actionable thresholds, clarifies differential feature contributions, and highlights the importance of comorbidity and demographic context in diagnostic evaluation. These findings support a patient-centred, data-driven framework for improving diagnostic consistency in clinical practice. Future work should focus on multi-site validation and temporal analyses to assess generalisability and stability of feature influences over time.

This chapter is concerned with the visualization of defeasible logic rules in the Semantic Web domain. Logic plays an important role in the development of the Semantic Web and defeasible reasoning seems to be a very suitable tool. However, it is too complex for an end-user, who often needs graphical trace and explanation mechanisms for the derived conclusions. Directed graphs can assist in this affair, by offering the notion of direction that appears to be extremely applicable for the representation of rule attacks and superiorities in defeasible reasoning. Their applicability, however, is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the connections between the nodes in the graph. In this chapter we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting the expressiveness and comprehensibility they offer, but also trying to leverage their major disadvantages. Finally, the chapter briefly presents a tool that implements this representation methodology.

In this paper we present our work in a real-time, context-aware system, applied in a smart classroom domain, which aims to assist its users after recognizing any occurring activity. We exploit the advantages of ontologies in order to model the context and introduce as well a method for extracting information from an ontology and using it in a machine learning dataset. This method enables real-time reasoning on high-level-activities recognition. We describe the overview of our system as well as a typical usage scenario to indicate how our system would react in this specific situation. An experimental evaluation of our system in a real, publicly available lecture is also presented. © 2012 Springer-Verlag.

The Extended Resource Description Framework has been proposed to equip RDF graphs with weak and strong negation, as well as derivation rules, increasing the expressiveness of ordinary RDF graphs. In parallel, the Modular Web framework enables collaborative and controlled reasoning in the Semantic Web. In this paper we show how to use the Modular Web Framework to capture the modular semantics for ERDF graphs, supporting local semantics and different points of view, local closed-world and open-world assumptions, and scoped negation-as-failure.