Prioritized conflict handing for logic programs
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Combining Horn rules and description logics in CARIN
Artificial Intelligence
Representation results for defeasible logic
ACM Transactions on Computational Logic (TOCL)
Explanations in Knowledge Systems: Design for Explainable Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
TRIPLE - A Query, Inference, and Transformation Language for the Semantic Web
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Description logic programs: combining logic programs with description logic
WWW '03 Proceedings of the 12th international conference on World Wide Web
A proposal for an owl rules language
Proceedings of the 13th international conference on World Wide Web
Argumentation Semantics for Defeasible Logic
Journal of Logic and Computation
Embedding defeasible logic into logic programming
Theory and Practice of Logic Programming
DR-Prolog: A System for Defeasible Reasoning with Rules and Ontologies on the Semantic Web
IEEE Transactions on Knowledge and Data Engineering
Explaining subsumption in description logics
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Explaining answers from the Semantic Web: the Inference Web approach
Web Semantics: Science, Services and Agents on the World Wide Web
On the decidability and complexity of integrating ontologies and rules
Web Semantics: Science, Services and Agents on the World Wide Web
Visualization of Proofs in Defeasible Logic
RuleML '08 Proceedings of the International Symposium on Rule Representation, Interchange and Reasoning on the Web
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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.