Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Ontology Matching
Semantic precision and recall for ontology alignment evaluation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
Markov network based ontology matching
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
OMEN: a probabilistic ontology mapping tool
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A survey of schema-based matching approaches
Journal on Data Semantics IV
Hi-index | 0.00 |
Augmented cognition on sensory data requires knowledge sources to expand the abilities of human senses. Ontologies are one of the most suitable knowledge sources, since they are designed to represent human knowledge and a number of ontologies on diverse domains can cover various objects in human life. To adopt ontologies as knowledge sources for augmented cognition, various ontologies for a single domain should be merged to prevent noisy and redundant information. This paper proposes a novel composite kernel to merge heterogeneous ontologies. The proposed kernel consists of lexical and graph kernels specialized to reflect structural and lexical information of ontology entities. In experiments, the composite kernel handles both structural and lexical information on ontologies more efficiently than other kernels designed to deal with general graph structures. The experimental results also show that the proposed kernel achieves the comparable performance with top-five systems in OAEI 2010.