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"/> Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil e-mail: fbaiao@puc-rio.br"/> Univ. Grenoble Alpes, INRIA, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France e-mail: Jerome.Euzenat@inria.fr"/>
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2020 Volume 35
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RESEARCH ARTICLE   Open Access    

Alin: improving interactive ontology matching by interactively revising mapping suggestions

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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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  • Cite this article

    Jomar Da Silva, Kate Revoredo, Fernanda Baião, Jérôme Euzenat. 2020. Alin: improving interactive ontology matching by interactively revising mapping suggestions. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888919000249
    Jomar Da Silva, Kate Revoredo, Fernanda Baião, Jérôme Euzenat. 2020. Alin: improving interactive ontology matching by interactively revising mapping suggestions. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888919000249
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RESEARCH ARTICLE   Open Access    

Alin: improving interactive ontology matching by interactively revising mapping suggestions

  • Received: 20 December 2018
  • Revised: 27 November 2019
  • Accepted: 04 December 2019
  • Published online: 20 January 2020
The Knowledge Engineering Review  35,  2020, 35(1)  |  Cite this article

Abstract: 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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    Acknowledgments

    • Jérôme Euzenat was partially funded by a CNPq Special Visiting Researcher grant (314782/2014-1).

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    Jomar Da Silva, Kate Revoredo, Fernanda Baião, Jérôme Euzenat. 2020. Alin: improving interactive ontology matching by interactively revising mapping suggestions. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888919000249
    Jomar Da Silva, Kate Revoredo, Fernanda Baião, Jérôme Euzenat. 2020. Alin: improving interactive ontology matching by interactively revising mapping suggestions. The Knowledge Engineering Review 35(1), doi: 10.1017/S0269888919000249
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