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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.893409/full |
| _version_ | 1818190754390999040 |
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| author | Peng Wang Peng Wang Yunyan Hu |
| author_facet | Peng Wang Peng Wang Yunyan Hu |
| author_sort | Peng Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-12T00:03:44Z |
| format | Article |
| id | doaj.art-3aad8b246a424be78de2594f610476c8 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-12T00:03:44Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-3aad8b246a424be78de2594f610476c82022-12-22T00:45:10ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-07-011310.3389/fgene.2022.893409893409Matching Biomedical Ontologies via a Hybrid Graph Attention NetworkPeng Wang0Peng Wang1Yunyan Hu2School of Computer Science and Engineering, Southeast University, Nanjing, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaSchool of Computer Science and Engineering, Southeast University, Nanjing, ChinaBiomedical 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.https://www.frontiersin.org/articles/10.3389/fgene.2022.893409/fullbiomedical ontologyontology matchinggraph attention networkembeddinghyperbolic attention |
| spellingShingle | Peng Wang Peng Wang Yunyan Hu Matching Biomedical Ontologies via a Hybrid Graph Attention Network Frontiers in Genetics biomedical ontology ontology matching graph attention network embedding hyperbolic attention |
| 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 | biomedical ontology ontology matching graph attention network embedding hyperbolic attention |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.893409/full |
| work_keys_str_mv | AT pengwang matchingbiomedicalontologiesviaahybridgraphattentionnetwork AT pengwang matchingbiomedicalontologiesviaahybridgraphattentionnetwork AT yunyanhu matchingbiomedicalontologiesviaahybridgraphattentionnetwork |