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Journal of Intelligent Information Systems

Erschienen in:

07.09.2020

A framework for evaluating ontology meta-matching approaches

verfasst von: Nicolas Ferranti, Jose Ronaldo Mouro, Fabricio Martins Mendonça, Jairo Francisco de Souza, Stenio Sa Rosario Furtado Soares

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 2/2021

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Abstract

Ontology matching has become a key issue to solve problems of semantic heterogeneity. Several researchers propose diverse techniques that can be used in distinct scenarios. Ontology meta-matching approaches are a specialization of ontology matching and have achieved good results in pairs of ontologies with different types of heterogeneities. However, developing a new ontology meta-matcher can be a costly process and a lot of experiments are often carried out to analyze the behavior of the matcher. This article presents a modularized framework that covers the main stages of the ontology meta-matching evaluation process. This framework aims to aid researchers to develop and analyze algorithms for ontology meta-matching, mainly metaheuristic-based supervised and unsupervised approaches. As the main contribution of the research, the framework proposed will facilitate the evaluation of ontology meta-matching approaches and, as the secondary contribution, a data provenance model that captures the main information generated and consumed throughout experiments is presented in the framework.

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Titel: A framework for evaluating ontology meta-matching approaches
verfasst von: Nicolas Ferranti
Jose Ronaldo Mouro
Fabricio Martins Mendonça
Jairo Francisco de Souza
Stenio Sa Rosario Furtado Soares
Publikationsdatum: 07.09.2020
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 2/2021
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-020-00615-8

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