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Matching Biomedical Ontologies via a Hybrid Graph Attention Network

Biomedical ontologies have been used extensively to formally define and organize biomedical terminologies, and these ontologies are typically manually created by biomedical experts. With more biomedical ontologies being built independently, matching them to address the problem of heterogeneity and i...

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Detalles Bibliográficos
Autores principales: Wang, Peng, Hu, Yunyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354052/
https://www.ncbi.nlm.nih.gov/pubmed/35938027
http://dx.doi.org/10.3389/fgene.2022.893409
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author Wang, Peng
Hu, Yunyan
author_facet Wang, Peng
Hu, Yunyan
author_sort Wang, Peng
collection PubMed
description Biomedical ontologies have been used extensively to formally define and organize biomedical terminologies, and these ontologies are typically manually created by biomedical experts. With more biomedical ontologies being built independently, matching them to address the problem of heterogeneity and interoperability has become a critical challenge in many biomedical applications. Existing matching methods have mostly focused on capturing features of terminological, structural, and contextual semantics in ontologies. However, these feature engineering-based techniques are not only labor-intensive but also ignore the hidden semantic relations in ontologies. In this study, we propose an alternative biomedical ontology-matching framework BioHAN via a hybrid graph attention network, and that consists of three techniques. First, we propose an effective ontology-enriching method that refines and enriches the ontologies through axioms and external resources. Subsequently, we use hyperbolic graph attention layers to encode hierarchical concepts in a unified hyperbolic space. Finally, we aggregate the features of both the direct and distant neighbors with a graph attention network. Experimental results on real-world biomedical ontologies demonstrate that BioHAN is competitive with the state-of-the-art ontology matching methods.
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spelling pubmed-93540522022-08-06 Matching Biomedical Ontologies via a Hybrid Graph Attention Network Wang, Peng Hu, Yunyan Front Genet Genetics Biomedical ontologies have been used extensively to formally define and organize biomedical terminologies, and these ontologies are typically manually created by biomedical experts. With more biomedical ontologies being built independently, matching them to address the problem of heterogeneity and interoperability has become a critical challenge in many biomedical applications. Existing matching methods have mostly focused on capturing features of terminological, structural, and contextual semantics in ontologies. However, these feature engineering-based techniques are not only labor-intensive but also ignore the hidden semantic relations in ontologies. In this study, we propose an alternative biomedical ontology-matching framework BioHAN via a hybrid graph attention network, and that consists of three techniques. First, we propose an effective ontology-enriching method that refines and enriches the ontologies through axioms and external resources. Subsequently, we use hyperbolic graph attention layers to encode hierarchical concepts in a unified hyperbolic space. Finally, we aggregate the features of both the direct and distant neighbors with a graph attention network. Experimental results on real-world biomedical ontologies demonstrate that BioHAN is competitive with the state-of-the-art ontology matching methods. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9354052/ /pubmed/35938027 http://dx.doi.org/10.3389/fgene.2022.893409 Text en Copyright © 2022 Wang and Hu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Wang, Peng
Hu, Yunyan
Matching Biomedical Ontologies via a Hybrid Graph Attention Network
title Matching Biomedical Ontologies via a Hybrid Graph Attention Network
title_full Matching Biomedical Ontologies via a Hybrid Graph Attention Network
title_fullStr Matching Biomedical Ontologies via a Hybrid Graph Attention Network
title_full_unstemmed Matching Biomedical Ontologies via a Hybrid Graph Attention Network
title_short Matching Biomedical Ontologies via a Hybrid Graph Attention Network
title_sort matching biomedical ontologies via a hybrid graph attention network
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354052/
https://www.ncbi.nlm.nih.gov/pubmed/35938027
http://dx.doi.org/10.3389/fgene.2022.893409
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