• DOI:10.5220/0005588200290037
• Corpus ID: 1126323

Exploiting Visual Similarities for Ontology Alignment

@inproceedings{Doulaverakis2015ExploitingVS,
  title={Exploiting Visual Similarities for Ontology Alignment},
  author={Charalampos Doulaverakis and Stefanos Vrochidis and Yiannis Kompatsiaris},
  booktitle={KEOD},
  year={2015}
}

• Charalampos Doulaverakis, S. Vrochidis, Y. Kompatsiaris
• Published in KEOD 12 November 2015
• Computer Science

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.