Semantic Web Reasoning with Conceptual Logic Programs

Heymans, Stijn; Nieuwenborgh, Davy Van; Vermeir, Dirk

doi:10.1007/978-3-540-30504-0_9

Cited by 18 publications

(21 citation statements)

References 16 publications

(16 reference statements)

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“…A different approach is presented in [23,22], which proposes Conceptual Logic Programming (CLP), an extension of answer set programming (i.e., Datalog ¬∨ ) towards infinite domains. In order to keep reasoning decidable, a syntactic restriction on CLP program rules is imposed.…”

Section: Loose Integrationmentioning

confidence: 99%

Integrating Ontologies and Rules: Semantic and Computational Issues

Rosati

2006

Reasoning Web

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Abstract. We present some recent results on the definition of logicbased systems integrating ontologies and rules. In particular, we take into account ontologies expressed in Description Logics and rules expressed in Datalog (and its nonmonotonic extensions). We first introduce the main issues that arise in the integration of ontologies and rules. In particular, we focus on the following aspects: (i) from the semantic viewpoint, ontologies are based on open-world semantics, while rules are typically interpreted under closed-world semantics. This semantic discrepancy constitutes an important obstacle for the definition of a meaningful combination of ontologies and rules; (ii) from the reasoning viewpoint, the interaction between an ontology and a rule component is very hard to handle, and does not preserve decidability and computational properties: e.g., starting from an ontology in which reasoning is decidable and a rule base in which reasoning is decidable, reasoning in the formal system obtained by integrating the two components may not be a decidable problem. Then, we briefly survey the main approaches for the integration of ontologies and rules, with special emphasis on how they deal with the above mentioned issues, and present in detail one of such approaches, i.e., DL+log. Finally, we illustrate the main open problems in this research area, pointing out what still prevents us from the development of both effective and expressive systems able to integrate ontologies and rules.

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Section: Loose Integrationmentioning

confidence: 99%

Integrating Ontologies and Rules: Semantic and Computational Issues

Rosati

2006

Reasoning Web

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“…One would thus wrongfully conclude that there can never be bad individuals. In [17], this was solved by considering open domains, i.e. the program may be grounded with any superset of the present constants: grounding with a universe {x , heather } yields bad (heather ) ← not good (heather ); bad (x ) ← not good (x ) and good (heather ) ← , which has an answer set {bad (x ), good (heather )}, correctly capturing the intended meaning of the program.…”

Section: Answer Set Programming With Open Domainsmentioning

confidence: 99%

“…We briefly recall the open answer set semantics from [17]. We call individual names constants and write them as lowercase letters, variables will be denoted with uppercase letters.…”

Section: Answer Set Programming With Open Domainsmentioning

confidence: 99%

“…Finally, note that satisfiability checking for DLPs under the open answer set semantics is undecidable since the undecidable domino problem [4] can be reduced to it [17].…”

Section: Answer Set Programming With Open Domainsmentioning

confidence: 99%

“…In [17], the syntax of DLPs was restricted in order to regain decidability of reasoning and to enable a reduction of reasoning to normal answer set programming, resulting in conceptual logic programs (CLPs). We recall the intuition and definition of CLPs.…”

Section: Adding Grounded Rules To Conceptual Logic Programsmentioning

confidence: 99%

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Nonmonotonic Ontological and Rule-Based Reasoning with Extended Conceptual Logic Programs

Heymans

Nieuwenborgh

Vermeir

2005

Lecture Notes in Computer Science

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Abstract. We present extended conceptual logic programs (ECLPs), for which reasoning is decidable and, moreover, can be reduced to finite answer set programming. ECLPs are useful to reason with both ontological and rule-based knowledge, which is illustrated by simulating reasoning in an expressive description logic (DL) equipped with DL-safe rules. Furthermore, ECLPs are more expressive in the sense that they enable nonmonotonic reasoning, a desirable feature in locally closed subareas of the Semantic Web.

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Answer Set Programming

Faber

Leone

Ricca

2008

Wiley Encyclopedia of Computer Science and Engineering

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Answer set programming (ASP) evolved from logic programming, deductive databases, knowledge representation, and nonmonotonic reasoning, and it serves as a flexible language for solving problems in a declarative way. The user does not need to provide an algorithm for solving the problem; rather, it is sufficient that (s)he specifies the properties of the desired solution for its computation. ASP is highly expressive and allows us to solve problems even beyond the complexity class NP. The main representation feature of ASP are rules, which are interpreted according to common sense principles. In ASP, one writes a program, that is, a collection of rules, which represent a problem to be solved. This program, together with some input, usually expressed using factual rules, possesses a collection of models (possibly also no model), called answer sets, which correspond to the solutions of the modeled problem. Efficient systems for computing answer sets are available and have stimulated the development of applications relying on ASP, in both academia and, business. The field of ASP is growing, and several extensions of the basic language have been proposed and used in applications.

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Semantic Web Reasoning with Conceptual Logic Programs

Cited by 18 publications

References 16 publications

Integrating Ontologies and Rules: Semantic and Computational Issues

Integrating Ontologies and Rules: Semantic and Computational Issues

Nonmonotonic Ontological and Rule-Based Reasoning with Extended Conceptual Logic Programs

Answer Set Programming

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