Machine Learning
ACM SIGKDD Explorations Newsletter
Bootstrapping semantics on the web: meaning elicitation from schemas
Proceedings of the 15th international conference on World Wide Web
Using Google distance to weight approximate ontology matches
Proceedings of the 16th international conference on World Wide Web
Semantic matching: algorithms and implementation
Journal on data semantics IX
A method to combine linguistic ontology-mapping techniques
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A survey of schema-based matching approaches
Journal on Data Semantics IV
Matching unstructured vocabularies using a background ontology
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
On the discovery of subsumption relations for the alignment of ontologies
Web Semantics: Science, Services and Agents on the World Wide Web
Candidate reduction and alignment improvement techniques used in aligning ontologies
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Techniques for discovering correspondences between ontologies
International Journal of Web and Grid Services
Ontology alignment for linked open data
ISWC'10 Proceedings of the 9th international semantic web 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
Discovering alignments in ontologies of linked data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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For the effective alignment of ontologies, the computation of equivalence relations between elements of ontologies is not enough: Subsumption relations play a crucial role as well. In this paper we propose the "Classification-Based Learning of Subsumption Relations for the Alignment of Ontologies" (CSR) method. Given a pair of concepts from two ontologies, the objective of CSR is to identify patterns of concepts' features that provide evidence for the subsumption relation among them. This is achieved by means of a classification task, using state of the art supervised machine learning methods. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series and discusses the potential of the method.