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Knowledge-graph-enabled biomedical entity linking: a survey

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Abstract

Biomedical Entity Linking (BM-EL) task, which aims to match biomedical mentions in articles to entities in a certain knowledge base (e.g., the Unified Medical Language System), draws dramatic attention in recent years. BM-EL can help to disambiguate medical terms and link to rich semantic information in the biomedical knowledge base, which can act as an essential means for many downstream applications. Although entity linking tasks have been investigated in the general domain and achieved great success, many challenges remain in the biomedical field, for instance, highly complex terminology, less training data, and entity ambiguity. In this survey, we categorize BM-EL methods into rule-based, machine learning, and deep learning models according to the development of the model paradigm and provide a comprehensive review of each approach. In-depth study of current BM-EL efforts, we group the model architectures into four categories: joint entity recognition and linking, graph-based global entity disambiguation, cross-lingual architectures, and model-efficiency improvement. We further introduce six well-established datasets that are commonly used for BM-EL tasks. Furthermore, we present a comparison of the different methods and discuss their advantages and disadvantages. Finally, we discuss the limitations of existing methods for BM-EL and discuss promising future research directions.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Jiyun Shi, Zhimeng Yuan, Wenxuan Guo & Meihui Zhang

  2. Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China

    Chen Ma

  3. Institute for Data Application and Management, China Academy of Industrial Internet, Beijing, 100102, China

    Jiehao Chen

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  1. Jiyun Shi
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Jiyun Shi: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data Curation, Writing for Original Draft. Zhimeng Yuan: Methodology, Validation, Formal analysis, Visualization. Wenxuan Guo: Methodology, Validation, Formal analysis. Meihui Zhang: Review and editing, Project administration. Chen Ma: Review and editing. Jiehao Chen: prepared the table, Investigation, Data Curation.

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Shi, J., Yuan, Z., Guo, W. et al. Knowledge-graph-enabled biomedical entity linking: a survey. World Wide Web 26, 2593–2622 (2023). https://doi.org/10.1007/s11280-023-01144-4

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