A framework for evaluating ontology meta-matching approaches

Ferranti, Nicolas; Mouro, Jose Ronaldo; Mendonça, Fabrício Martins; Souza, Jairo; Soares, Stênio Sã Rosário Furtado

doi:10.1007/s10844-020-00615-8

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…Both GA and PPA are approaches for global optimization, to check the behavior of local search metaheuristics this research also evaluated a GRASP-based approach. GRASP-based approach consists of an interactive heuristic that tries to improve a specific solution at each iteration; full implementation is available in [24].…”

Section: Popular Methods Used In Omm Approachesmentioning

confidence: 99%

An experimental analysis on evolutionary ontology meta-matching

2021

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Every year, new ontology matching approaches have been published to address the heterogeneity problem in ontologies. It is well known that no one is able to stand out from others in all aspects. An ontology meta-matcher combines different alignment techniques to explore various aspects of heterogeneity to avoid the alignment performance being restricted to some ontology characteristics. The meta-matching process consists of several stages of execution, and sometimes the contribution/cost of each algorithm is not clear when evaluating an approach. This article presents the evaluation of solutions commonly used in the literature in order to provide more knowledge about the ontology meta-matching problem. Results showed that the more characteristics of the entities that can be captured by similarity measures set, the greater the accuracy of the model. It was also possible to observe the good performance and accuracy of local search-based meta-heuristics when compared to global optimization meta-heuristics. Experiments with different objective functions have shown that semi-supervised methods can shorten the execution time of the experiment but, on the other hand, bring more instability to the result.

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Section: Popular Methods Used In Omm Approachesmentioning

confidence: 99%

An experimental analysis on evolutionary ontology meta-matching

2021

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“…The current meta-heuristics approaches treat ontology matching problem as a continuous optimization problem called meta-matching problem, which requires maintaining all the alignments determined by different ontology matchers and optimizing their aggregation Ferranti, Mouro, Mendonça, de Souza, and Soares (2021). This process is time and memory consuming.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm

Lian¹,

Li²

2023

Preprint

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Ontologies provide a standardized approach to knowledge representation that can be shared across various domains. By extracting heterogeneous ontology alignment, E-businesses can efficiently exchange information and enhance communication, decision-making, and reduce data integration costs. In this study, we investigate the heterogeneous ontology alignment extraction problem for E-business, which aims to determine an optimal concept pair set with the highest f-measure value. Given the alignment extraction’s inherent complexity, we use a Genetic Algorithm (GA) to address it. In particular, we first model the HOAEP as a multi-modal problem with sparse solutions and then propose a Compact Co-Evolutionary Niching Genetic Algorithm (CCNGA) to address it. CCNGA first employs probability distribution estimation to simplify population representation, and then uses three evolutionary strategies to simultaneously search for the global optimum. The experimental testing cases include OAEI’s Conference track and three real E-business ontologies, and T-test results demonstrate that CCNGA significantly outperforms other state-of-the-art ontology alignment extraction techniques.

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An Improved Fireworks Algorithm for Ontology Matching

Shi

2022

Lecture Notes in Electrical Engineering

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A framework for evaluating ontology meta-matching approaches

Cited by 7 publications

References 34 publications

An experimental analysis on evolutionary ontology meta-matching

An experimental analysis on evolutionary ontology meta-matching

Heterogeneous Ontology Alignment Extraction For E-Business Through Compact Co-Evolutionary Niching Genetic Algorithm

An Improved Fireworks Algorithm for Ontology Matching

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