Learning Subsumption Relations with CSR: a Classification based Method for the Alignment of Ontologies

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 (here, properties) that provide evidence for the subsumption relation among these concepts. This is achieved by means of a classification task using decision trees. For the learning of the decision trees, the proposed method generates training datasets from the source ontologies’, considering each ontology in isolation. The paper describes thoroughly the method, provides experimental results for computing subsumption relations over an extended version of the OAEI 2006 benchmarking series and discusses the potential of the method.

• Type: Conference
• Year: 2007
• Where: SEMWEB
• Authors: Vassilis Spiliopoulos, Alexandros G. Valarakos, George A. Vouros, Vangelis Karkaletsis
• Keywords: Decision Trees, Method Generates Training, SEMWEB 2007, Subsumption Relations