Semantic Web Dog Food

Presented at: 5th European Semantic Web Conference (ESWC2008)

by Vassilis Spiliopoulos, Alexandros Valarakos, George Vouros

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. For the learning of the classifiers, CSR generates training datasets from the source ontologies', considering each ontology in isolation: This allows the method to tune itself to the idiosyncrasies of each of the source ontologies. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series and discusses the potential of the method.

Keywords: binary classification, ontology alignment, subsumption, supervised machine learning, Data Integration, Machine Learning, Ontology (computer science), Ontology (Computer Science), Ontology alignment, Ontology Alignment, Semantic Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2008/paper/107
Same as: http://revyu.com/things/eswc-2008-paper-csr-discovering-ontologies
Same as: http://semanticweb.org/id/CSR-3A_Discovering_Subsumption_Relations_for_the_Alignment_of_Ontologies

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