Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/eswc2008/paper/107> ?p ?o. }
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- 107 creator alexandros-valarakos.
- 107 creator george-vouros.
- 107 creator vassilis-spiliopoulos.
- 107 type InProceedings.
- 107 label "CSR: Discovering Subsumption Relations for the Alignment of Ontologies".
- 107 sameAs 107.
- 107 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.".
- 107 hasAuthorList authorList.
- 107 hasTopic Data_integration.
- 107 hasTopic Machine_learning.
- 107 hasTopic Ontology_%28computer_science%29.
- 107 hasTopic Ontology_alignment.
- 107 hasTopic Semantic_Web.
- 107 isPartOf proceedings.
- 107 keyword "binary classification".
- 107 keyword "ontology alignment".
- 107 keyword "subsumption".
- 107 keyword "supervised machine learning".
- 107 title "CSR: Discovering Subsumption Relations for the Alignment of Ontologies".