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Towards evaluating complex ontology alignments

Part of: Ontology Alignment: Algorithms and Evaluation

Published online by Cambridge University Press:  29 May 2020

Lu Zhou Open the ORCID record for Lu Zhou [Opens in a new window] ,
Elodie Thiéblin ,
Michelle Cheatham Open the ORCID record for Michelle Cheatham [Opens in a new window] ,
Daniel Faria ,
Catia Pesquita ,
Cassia Trojahn  and
Ondřej Zamazal
Lu Zhou
Affiliation:
Data Semantics Laboratory, Kansas State University, Manhattan, USA; e-mail: luzhou@ksu.edu
Elodie Thiéblin
Affiliation:
IRIT & Université de Toulouse 2 Jean Jaurès, Toulouse, France; e-mails: elodie.thieblin@irit.fr, cassia.trojahn@irit.fr
Michelle Cheatham
Affiliation:
Wright State University, Dayton, USA; e-mail: michelle.cheatham@wright.edu
Daniel Faria
Affiliation:
Instituto Gulbenkian de Ciência, Oeiras, Portugal; e-mail: dfaria@igc.gulbenkian.pt
Catia Pesquita
Affiliation:
Lasige, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; e-mail: clpesquita@fc.ul.pt
Cassia Trojahn
Affiliation:
IRIT & Université de Toulouse 2 Jean Jaurès, Toulouse, France; e-mails: elodie.thieblin@irit.fr, cassia.trojahn@irit.fr
Ondřej Zamazal
Affiliation:
Faculty of Informatics and Statistics, University of Economics, Prague, Czech Republic; e-mail: ondrej.zamazal@vse.cz
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Abstract

The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.

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Type
Review
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e21
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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