Data Semantics Laboratory, Kansas State University, Manhattan, USA; e-mail: luzhou@ksu.edu"/> IRIT & Université de Toulouse 2 Jean Jaurès, Toulouse, France; e-mails: elodie.thieblin@irit.fr, cassia.trojahn@irit.fr"/> Wright State University, Dayton, USA; e-mail: michelle.cheatham@wright.edu"/> Instituto Gulbenkian de Ciência, Oeiras, Portugal; e-mail: dfaria@igc.gulbenkian.pt"/> Lasige, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal; e-mail: clpesquita@fc.ul.pt"/> Faculty of Informatics and Statistics, University of Economics, Prague, Czech Republic; e-mail: ondrej.zamazal@vse.cz"/>
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2020 Volume 35
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Towards evaluating complex ontology alignments

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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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  • Cite this article

    Lu Zhou, Elodie Thiéblin, Michelle Cheatham, Daniel Faria, Catia Pesquita, Cassia Trojahn, Ondřej Zamazal. 2020. Towards evaluating complex ontology alignments. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000168
    Lu Zhou, Elodie Thiéblin, Michelle Cheatham, Daniel Faria, Catia Pesquita, Cassia Trojahn, Ondřej Zamazal. 2020. Towards evaluating complex ontology alignments. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000168

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REVIEW   Open Access    

Towards evaluating complex ontology alignments

Abstract: 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.

    • DF was funded by the ELIXIR-EXCELERATE project (INFRADEV-3-2015). CP was funded by the Fundação para a Ciência e Tecnologia through the funding of the LaSIGE research unit (ref.UID/CEC/00408/2013) and project PTDC/EEI-ESS/4633/2014. OZ was supported by the CSF grant no. 18-23964S and by long-term institutional support of research activities by Faculty of Informatics and Statistics, University of Economics, Prague.

    • http://oaei.ontologymatching.org/2018/complex/.

    • http://www.cs.ox.ac.uk/isg/projects/Optique/oaei/oa4qa/index.html.

    • http://www.music.tuc.gr/projects/sw/sparql-rw/.

    • © The Author(s), 2020. Published by Cambridge University Press2020Cambridge University Press
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    Lu Zhou, Elodie Thiéblin, Michelle Cheatham, Daniel Faria, Catia Pesquita, Cassia Trojahn, Ondřej Zamazal. 2020. Towards evaluating complex ontology alignments. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000168
    Lu Zhou, Elodie Thiéblin, Michelle Cheatham, Daniel Faria, Catia Pesquita, Cassia Trojahn, Ondřej Zamazal. 2020. Towards evaluating complex ontology alignments. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888920000168
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