Navya Martin Kollapally , James Geller , Vipina Kuttichi Keloth , Zhe He , Julia Xu
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引用次数: 0
Abstract
Objective
Ontologies are essential for representing the knowledge of a domain. To make ontologies useful, they must encompass a comprehensive domain view. To achieve ontology enrichment, there is a need to discover new concepts to be added, either because they were missed in the first place, or the state-of-the-art has advanced to develop new real-world concepts. Our goal is to develop an automatic enrichment pipeline using a seed ontology, a Large Language Model (LLM), and source of text. The pipeline is applied to the domain of Social Determinants of Health (SDoH), using PubMed as a source of concepts. In this work, the applicability and effectiveness of the enrichment pipeline is demonstrated by extending the SDoH Ontology called SOHOv1, however our methodology could be used in other domains as well.
Methods
We first retrieved PubMed abstracts of candidate articles with existing SOHOv1 concepts as search terms. Next, we used GPT-4-1201 to extract semantic triples from the abstracts. We identified concepts from these triples utilizing lexical, semantic, and knowledge network-based filtering. We also compared the granularity of semantic triples extracted with our method to the triples in the SemMedDB (Semantic MEDLINE Database). The results were evaluated by human experts and standard ontology tools for checking consistency and semantic correctness.
Results
We expanded SOHOv1, which contained 173 concepts and 585 axioms, including 207 logical axioms to SOHOv2, which contains 572 concepts, 1,542 axioms, including 725 logical axioms. Our methods identified more concepts than those extracted from SemMedDB for the same task. While we have shown the feasibility of our approach for an SDoH ontology, the methodology is generalizable to other ontologies with an existing seed ontology and text corpus.
Conclusions
The contributions of this work are: Extracting semantic triples from PubMed abstracts using GPT-4-1201 utilizing prompt chaining; showing the superiority of triples from GPT-4-1201 over triples from SemMedDB for SDoH; using lexical and semantic similarity search techniques with knowledge network-based search to identify the concepts to be added to the ontology; confirming the quality of the new concepts with human experts.
期刊介绍:
The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.