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
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 statistical and schema independent approach to identify equivalent properties on linked data
Proceedings of the 9th International Conference on Semantic Systems
Discovering alignments in ontologies of linked data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Despite the increase in the number of linked instances in the Linked Data Cloud in recent times, the absence of links at the concept level has resulted in heterogenous schemas, challenging the interoperability goal of the Semantic Web. In this paper, we address this problem by finding alignments between concepts from multiple Linked Data sources. Instead of only considering the existing concepts present in each ontology, we hypothesize new composite concepts defined as disjunctions of conjunctions of (RDF) types and value restrictions, which we call restriction classes, and generate alignments between these composite concepts. This extended concept language enables us to find more complete definitions and to even align sources that have rudimentary ontologies, such as those that are simple renderings of relational databases. Our concept alignment approach is based on analyzing the extensions of these concepts and their linked instances. Having explored the alignment of conjunctive concepts in our previous work, in this paper, we focus on concept coverings (disjunctions of restriction classes). We present an evaluation of this new algorithm to Geospatial, Biological Classification, and Genetics domains. The resulting alignments are useful for refining existing ontologies and determining the alignments between concepts in the ontologies, thus increasing the interoperability in the Linked Open Data Cloud.