Extended Tversky Similarity for Resolving Terminological Heterogeneities across Ontologies

Duy Hoa Ngo (1) , Zohra Bellahsene (2) , Konstantin Todorov (3)

1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 ZENITH - Scientific Data Management
3 WEB3 - WEB Architecture x Semantic WEB x WEB of Data

We propose a novel method to compute similarity between cross-ontology concepts based on the amount of overlap of the information content of their labels. We extend Tversky's similarity measure by using the information content of each term within an ontology label both for the similarity computation and for the weight assignment to tokens. The approach is suitable for handling compound labels. Our experiments showed that it outperforms existing terminological similarity measures for the ontology matching task.

Mots clés: Weight assignment

Domaines: Web; Recherche d'information [cs.IR]; Intelligence artificielle [cs.AI]; Base de données [cs.DB]

Duy Hoa Ngo, Zohra Bellahsene, Konstantin Todorov. Extended Tversky Similarity for Resolving Terminological Heterogeneities across Ontologies. ODBase - OTM, Jan 2013, Gratz, Austria. pp.711-718, ⟨10.1007/978-3-642-41030-7_52⟩. ⟨hal-01987782⟩