A framework for evaluating ontology meta-matching approaches
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A framework for evaluating ontology meta-matching approaches Nicolas Ferranti1,2 · Jose Ronaldo Mouro1,2 · Fabricio Martins Mendonc¸a1,2 · Jairo Francisco de Souza1,2 · Stenio Sa Rosario Furtado Soares2 Received: 12 June 2020 / Revised: 12 August 2020 / Accepted: 12 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Ontology matching · Evolutionary ontology matching · Ontology meta-matching · Metaheuristics · Data provenance Nicolas Ferranti
[email protected] Jose Ronaldo Mouro [email protected] Fabricio Martins Mendonc¸a [email protected] Jairo Francisco de Souza [email protected] Stenio Sa Rosario Furtado Soares [email protected] 1
LApIC Research Group, Department of Computer Science, Campus Universit´ario, 36.060-900, Juiz de Fora, Brazil
2
Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil
Journal of Intelligent Information Systems
1 Introduction Throughout the evolution of Web, several challenges have emerged to optimize information exchange and facilitate the consumption of Web-published data. Ontologies have emerged to determine a well-defined meaning for data and to reduce semantic heterogeneity problems when this data is consumed (De Souza et al. 2019). However, ontologies themselves have become the cause of a new problem in data semantics: using more than one ontology in an application can lead to ambiguity in data interpretation, mainly because ontologies are built by engineers with different needs and domain views (Acampora et al. 2013). The ontology matching (OM) problem consists of finding relationships between entities of different ontologies (Faria et al. 2013). Once multiple relationships are established, knowledge of ontologies can be used together without semantic heterogeneity, facilitating the development of increasingly intelligent applications. Although the OM problem is relatively recent, several approaches have alre
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