Exploiting visual similarities for ontology alignment

Doulaverakis, Charalampos; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:http://dx.doi.org/10.5220/0005588200290037

Exploiting visual similarities for ontology alignment

Citation

Doulaverakis C, Vrochidis S, Kompatsiaris I. Exploiting visual similarities for ontology alignment. In: Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) KEOD; 2015 Nov 12-14; Lisbon, Portugal. Portugal: SCITEPRESS; 2015. vol. 2. p. 29-37. DOI: 10.5220/0005588200290037

Abstract

Ontology alignment is the process where two different ontologies that usually describe similar domains are ’aligned’, i.e. a set of correspondences between their entities, regarding semantic equivalence, is determined. In order to identify these correspondences several methods and metrics that measure semantic equivalence have been proposed in literature. The most common features that these metrics employ are string-, lexical-, structure- and semantic-based similarities for which several approaches have been developed. However, what hasn’t been investigated is the usage of visual-based features for determining entity similarity in cases where images are associated with concepts. Nowadays the existence of several resources (e.g. ImageNet) that map lexical concepts onto images allows for exploiting visual similarities for this purpose. In this paper, a novel approach for ontology matching based on visual similarity is presented. Each ontological entity is associated with sets of images, retrieved through ImageNet or web-based search, and state of the art visual feature extraction, clustering and indexing for computing the similarity between entities is employed. An adaptation of a popular Wordnet-based matching algorithm to exploit the visual similarity is also proposed. Our method is compared with traditional metrics against a standard ontology alignment benchmark dataset and demonstrates promising results.