A theory of diagnosis from first principles
Artificial Intelligence
Debugging and repair of owl ontologies
Debugging and repair of owl ontologies
Knowledge integration for description logics
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Scalable semantic retrieval through summarization and refinement
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Reasoning with inconsistent ontologies
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The summary abox: cutting ontologies down to size
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Towards knowledge acquisition from information extraction
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Computing minimum cost diagnoses to repair populated DL-based ontologies
Proceedings of the 17th international conference on World Wide Web
Assessing trust in uncertain information
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Evaluation of techniques for inconsistency handling in OWL 2 QL ontologies
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
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The approach of using ontology reasoning to cleanse the output of information extraction tools was first articulated in SemantiClean. A limiting factor in applying this approach has been that ontology reasoning to find inconsistencies does not scale to the size of data produced by information extraction tools. In this paper, we describe techniques to scale inconsistency detection, and illustrate the use of our techniques to produce a consistent subset of a knowledge base with several thousand inconsistencies.