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Alin: improving interactive ontology matching by interactively revising mapping suggestions

Part of: Ontology Alignment: Algorithms and Evaluation

Published online by Cambridge University Press:  20 January 2020

Jomar Da Silva Open the ORCID record for Jomar Da Silva [Opens in a new window] ,
Kate Revoredo ,
Fernanda Baião Open the ORCID record for Fernanda Baião [Opens in a new window]  and
Jérôme Euzenat
Jomar Da Silva
Affiliation:
Graduate Program in Informatics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil e-mails: jomar.silva@uniriotec.br, katerevoredo@ppgi.ufrj.br
Kate Revoredo
Affiliation:
Graduate Program in Informatics, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil e-mails: jomar.silva@uniriotec.br, katerevoredo@ppgi.ufrj.br
Fernanda Baião
Affiliation:
Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil e-mail: fbaiao@puc-rio.br
Jérôme Euzenat
Affiliation:
Univ. Grenoble Alpes, INRIA, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France e-mail: Jerome.Euzenat@inria.fr
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Abstract

Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

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Type
Research Article
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e1
Copyright
© Cambridge University Press 2020

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