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dc.contributor.author Doulaverakis, Charalampos
dc.contributor.author Vrochidis, Stefanos
dc.contributor.author Kompatsiaris, Ioannis
dc.date.accessioned 2017-02-02T14:26:51Z
dc.date.available 2017-02-02T14:26:51Z
dc.date.issued 2015
dc.identifier.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
dc.identifier.uri http://hdl.handle.net/10230/28035
dc.description.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.
dc.description.sponsorship This work was supported by MULTISENSOR (contract no. FP7-610411) and KRISTINA (contract no./nH2020-645012) projects, partially funded by the European Commission.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher SCITEPRESS
dc.relation.ispartof 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.
dc.rights © 2015 by SCITEPRESS – Science and Technology Publications, Lda.
dc.title Exploiting visual similarities for ontology alignment
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.5220/0005588200290037
dc.subject.keyword ontology alignment
dc.subject.keyword visual similarity
dc.subject.keyword imageNet
dc.subject.keyword wordnet
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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