%0 Conference Paper %1 spiliopoulos2008discovering %A Spiliopoulos, Vassilis %A Valarakos, Alexandros %A Vouros, George %B Proceedings of the 5th European Semantic Web Conference %C Berlin, Heidelberg %D 2008 %E Hauswirth, Manfred %E Koubarakis, Manolis %E Bechhofer, Sean %I Springer Verlag %K classification supervised machine learning subsumption alignment ontology binary ontology-alignment %T CSR: Discovering Subsumption Relations for the Alignment of Ontologies %U http://data.semanticweb.org/conference/eswc/2008/papers/107 %X 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.

@inproceedings{spiliopoulos2008discovering, abstract = {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.}, added-at = {2008-05-28T14:49:55.000+0200}, address = {Berlin, Heidelberg}, author = {Spiliopoulos, Vassilis and Valarakos, Alexandros and Vouros, George}, biburl = {https://www.bibsonomy.org/bibtex/27c1cedbbff889c129dbb35a5ae7d36c4/eswc2008}, booktitle = {Proceedings of the 5th European Semantic Web Conference}, editor = {Hauswirth, Manfred and Koubarakis, Manolis and Bechhofer, Sean}, interhash = {310b8b033edc29afde6fe841f7468e15}, intrahash = {7c1cedbbff889c129dbb35a5ae7d36c4}, keywords = {classification supervised machine learning subsumption alignment ontology binary ontology-alignment}, month = {June}, publisher = {Springer Verlag}, series = {LNCS}, timestamp = {2008-05-28T14:49:55.000+0200}, title = {CSR: Discovering Subsumption Relations for the Alignment of Ontologies}, url = {http://data.semanticweb.org/conference/eswc/2008/papers/107}, year = 2008 }