Authors:
Charalampos Doulaverakis
;
Stefanos Vrochidis
and
Ioannis Kompatsiaris
Affiliation:
Information Technologies Institute, Greece
Keyword(s):
Ontology Alignment, Visual Similarity, ImageNet, Wordnet.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaboration and e-Services
;
e-Business
;
Enterprise Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontology Matching and Alignment
;
Semantic Web
;
Soft Computing
;
Symbolic Systems
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 image
s, 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.
(More)