Ontology Matching
An empirical study of instance-based ontology matching
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
CSR: discovering subsumption relations for the alignment of ontologies
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Ontology alignment for linked open data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Linking and building ontologies of linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Schema Matching and Mapping
Contextual ontology alignment of LOD with an upper ontology: a case study with proton
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
A formal semantics for weighted ontology mappings
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Discovering concept coverings in ontologies of linked data sources
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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Recently, large amounts of data are being published using Semantic Web standards. Simultaneously, there has been a steady rise in links between objects from multiple sources. However, the ontologies behind these sources have remained largely disconnected, thereby challenging the interoperability goal of the Semantic Web. We address this problem by automatically finding alignments between concepts from multiple linked data sources. Instead of only considering the existing concepts in each ontology, we hypothesize new composite concepts, defined using conjunctions and disjunctions of (RDF) types and value restrictions, and generate alignments between them. In addition, our techniques provide a novel method for curating the linked data web by pointing to likely incorrect or missing assertions. Our approach provides a deeper understanding of the relationships between linked data sources and increases the interoperability among previously disconnected ontologies.