Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between...

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Autores principales:	Sohaib Al-Yadumi, Wei-Wei Goh, Ee-Xion Tan, Noor Zaman Jhanjhi, Patrice Boursier
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	aggregation strategy background knowledge biomedical ontologies indirect matching mapping composition ontology alignment Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/79c5652080064702bfdbe10e7c2670fb
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spelling	oai:doaj.org-article:79c5652080064702bfdbe10e7c2670fb2021-11-25T17:58:45ZMultimatcher Model to Enhance Ontology Matching Using Background Knowledge10.3390/info121104872078-2489https://doaj.org/article/79c5652080064702bfdbe10e7c2670fb2021-11-01T00:00:00Zhttps://www.mdpi.com/2078-2489/12/11/487https://doaj.org/toc/2078-2489Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.Sohaib Al-YadumiWei-Wei GohEe-Xion TanNoor Zaman JhanjhiPatrice BoursierMDPI AGarticleaggregation strategybackground knowledgebiomedical ontologiesindirect matchingmapping compositionontology alignmentInformation technologyT58.5-58.64ENInformation, Vol 12, Iss 487, p 487 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	aggregation strategy background knowledge biomedical ontologies indirect matching mapping composition ontology alignment Information technology T58.5-58.64
spellingShingle	aggregation strategy background knowledge biomedical ontologies indirect matching mapping composition ontology alignment Information technology T58.5-58.64 Sohaib Al-Yadumi Wei-Wei Goh Ee-Xion Tan Noor Zaman Jhanjhi Patrice Boursier Multimatcher Model to Enhance Ontology Matching Using Background Knowledge
description	Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.
format	article
author	Sohaib Al-Yadumi Wei-Wei Goh Ee-Xion Tan Noor Zaman Jhanjhi Patrice Boursier
author_facet	Sohaib Al-Yadumi Wei-Wei Goh Ee-Xion Tan Noor Zaman Jhanjhi Patrice Boursier
author_sort	Sohaib Al-Yadumi
title	Multimatcher Model to Enhance Ontology Matching Using Background Knowledge
title_short	Multimatcher Model to Enhance Ontology Matching Using Background Knowledge
title_full	Multimatcher Model to Enhance Ontology Matching Using Background Knowledge
title_fullStr	Multimatcher Model to Enhance Ontology Matching Using Background Knowledge
title_full_unstemmed	Multimatcher Model to Enhance Ontology Matching Using Background Knowledge
title_sort	multimatcher model to enhance ontology matching using background knowledge
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/79c5652080064702bfdbe10e7c2670fb
work_keys_str_mv	AT sohaibalyadumi multimatchermodeltoenhanceontologymatchingusingbackgroundknowledge AT weiweigoh multimatchermodeltoenhanceontologymatchingusingbackgroundknowledge AT eexiontan multimatchermodeltoenhanceontologymatchingusingbackgroundknowledge AT noorzamanjhanjhi multimatchermodeltoenhanceontologymatchingusingbackgroundknowledge AT patriceboursier multimatchermodeltoenhanceontologymatchingusingbackgroundknowledge
_version_	1718411752731836416

Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

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