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

by: Vassilis Spiliopoulos, Alexandros Valarakos, George Vouros, and Vangelis Karkaletsis

In: Proceedings of the Workshop on Ontology Matching OM2007 at ISWC/ASWC2007, Busan, South Korea (November 2007) .

Abstract

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.

BibTeX record

@inproceedings{Spiliopoulos/2007/Learning,
  abstract = {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.},
  added-at = {2007-11-07T19:19:06.000+0100},
  author = {Spiliopoulos, Vassilis and Valarakos, Alexandros and Vouros, George and Karkaletsis, Vangelis},
  biburl = {http://www.bibsonomy.org/bibtex/2ccad05209281edbbfddd25fddd210ffa/iswc2007},
  booktitle = {Proceedings of the Workshop on Ontology Matching (OM2007) at ISWC/ASWC2007, Busan, South Korea},
  crossref = {http://data.semanticweb.org/workshop/om/2007/proceedings},
  editor = {Shvaiko, Pavel and Euzenat, Jérôme and Giunchiglia, Fausto and He, Bin},
  interhash = {185fe72cb36484b6445bab65b196852a},
  intrahash = {ccad05209281edbbfddd25fddd210ffa},
  keywords = {2007 alignment iswc learning method ontology relations subsumption workshop_om},
  month = {November},
  timestamp = {2007-11-07T19:19:06.000+0100},
  title = {Learning Subsumption Relations with CSR: A Classification-based Method for the Alignment of Ontologies},
  year = 2007
}

Endnote record

%0 Conference Paper
%1 Spiliopoulos/2007/Learning
%A Spiliopoulos, Vassilis
%A Valarakos, Alexandros
%A Vouros, George
%A Karkaletsis, Vangelis
%B Proceedings of the Workshop on Ontology Matching (OM2007) at ISWC/ASWC2007, Busan, South Korea
%D 2007
%E Shvaiko, Pavel
%E Euzenat, Jérôme
%E Giunchiglia, Fausto
%E He, Bin
%K 
%T Learning Subsumption Relations with CSR: A Classification-based Method for the Alignment of Ontologies
%X 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.