oMAP: Results of the Ontology Alignment Contest (Contributo in atti di convegno)

Type
Label
  • oMAP: Results of the Ontology Alignment Contest (Contributo in atti di convegno) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Alternative label
  • Straccia U.; Troncy R. (2005)
    oMAP: Results of the Ontology Alignment Contest
    in 3rd Workshop on Integrating Ontologies: International Conference on Knowledge Capture (K-CAP-05), Banff
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Straccia U.; Troncy R. (literal)
Pagina inizio
  • 92 (literal)
Pagina fine
  • 96 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ceur-ws.org/Vol-156/paper14.pdf (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 156 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • (Canada, October, 2-5 2005). Proceedings, Vol. 156, pp. 92-96. Benjamin Ashpole and Marc Ehrig and Jérôme Euzenat and Heiner Stuckenschmidt (eds). CE (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • ABSTRACT: This paper introduces a method and a tool for automatically aligning OWL ontologies, a crucial step for achieving the interoperability of heterogeneous systems in the Semantic Web. Different components are combined for finding suitable mapping candidates (together with their weights), and the set of rules with maximum matching probability is selected. Machine learning-based classifiers and a new classifier using the structure and the semantics of the OWL ontologies are proposed. Our method has been implemented and evaluated on an independent test set provided by the international ontology alignment contest EON. We provide the results of this evaluation with respect to the other competitors. (literal)
Note
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISTI-CNR (literal)
Titolo
  • oMAP: Results of the Ontology Alignment Contest (literal)
Abstract
  • This paper introduces a method and a tool for automatically aligning OWL ontologies, a crucial step for achieving the interoperability of heterogeneous systems in the Semantic Web. Different components are combined for finding suitable mapping candidates (together with their weights), and the set of rules with maximum matching probability is selected. Machine learning-based classifiers and a new classifier using the structure and the semantics of the OWL ontologies are proposed. Our method has been implemented and evaluated on an independent test set provided by the international ontology alignment contest EON. We provide the results of this evaluation with respect to the other competitors. (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Autore CNR di
Prodotto
Insieme di parole chiave di
data.CNR.it