Authors:
Ignacio Huitzil
1
;
Fernando Bobillo
2
;
Eduardo Mena
2
;
Carlos Bobed
3
and
Jesús Bermúdez
4
Affiliations:
1
University of Zaragoza, Zaragoza and Spain
;
2
University of Zaragoza, Zaragoza, Spain, Aragon Institute of Engineering Research (I3A), Zaragoza and Spain
;
3
University of Zaragoza, Zaragoza, Spain, everis / NTT Data, Zaragoza and Spain
;
4
University of the Basque Country (UPV/EHU), Donostia-San Sebastián and Spain
Keyword(s):
Ontology Alignment, Hyponymy Relationships, Semantic Web.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Cloud Computing
;
Data Engineering
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Semantic Web Technologies
;
Services Science
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Using intelligent techniques to automatically compute semantic relationships across ontologies is a challenging task that is necessary in many real-world applications requiring the integration of semantic information coming from different sources. However, most of the work in the field is restricted to the discovery of synonymy relationships. Hyponymy relationships, although in the real world they are more frequent than synonymy, have not received similar attention. In this paper, we evaluate a technique based on shared properties used in the discovery of hyponymy relationships and identify some limitations of ontology sets commonly used as benchmarks. We also argue that new lexical similarity measures are needed and discuss a preliminary proposal.