ArticleJournal of biomedical semantics2018
Biomedical ontology alignment: an approach based on representation learning.
Article in Journal of biomedical semantics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness.PloS one · 2024Article
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Automatic Mapping of Terminology Items with Transformers.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning.IEEE journal of translational engineering in health and medicine · 2023Article
- Performance assessment of ontology matching systems for FAIR data.Journal of biomedical semantics · 2022Article
- Context-Enriched Learning Models for Aligning Biomedical Vocabularies at Scale in the UMLS Metathesaurus.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2022Article
- Matching Biomedical Ontologies: Construction of Matching Clues and Systematic Evaluation of Different Combinations of Matchers.JMIR medical informatics · 2021Article
- Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2021Article
- Lab indicators standardization method for the regional healthcare platform: a case study on heart failure.BMC medical informatics and decision making · 2020Article
- Assessing the enrichment of dietary supplement coverage in the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Bootstrapping Adversarial Learning of Biomedical Ontology Alignments.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
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Authors and funding
4 authors.
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Abstract
backgroundWhile representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.
resultsAn ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.
conclusionsOur proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.