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In this paper, we introduce BioLinkerAI, a neuro-symbolic approach tailored for biomedical entity linking. Traditional domain-specific entity linking approaches necessitate substantial labeled-training datasets and present challenges when adapting to each new dataset or setting. BioLinkerAI integrates symbolic methodologies with sub-symbolic models to mitigate the constraints of limited training data availability. The symbolic component utilizes a rule-based entity extraction mechanism, underpinned by an extensive set of linguistic and domain-specific rules. Concurrently, the sub-symbolic component employs a Large Language Model (LLM) to achieve precise candidate disambiguation. This mechanism enhances entity linking accuracy, especially when a single entity in a knowledge base such as UMLS aligns with multiple terms, leveraging the contextual intelligence encapsulated within the LLM’s embeddings. Empirical evaluations conducted on a range of biomedical benchmarks demonstrate the superior performance of BioLinkerAI. Notably, it surpasses existing benchmarks in entity linking accuracy (e.g., achieving an improvement from 65.4 (best baseline) to 78.5 (our model) on unseen data accuracy, representing the most stringent evaluation paradigm). Additionally, BioLinkerAI consistently performs adeptly on both structured sentences and individual keywords. To facilitate broader utilization, the source code, datasets, and a public API are available ( https://github.com/SDM-TIB/BioLinkerAI ).

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BioLinkerAI: Capturing Knowledge Using LLMs to Enhance Biomedical Entity Linking

  • 会议论文

  • 发表日期：

    2025年

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  • Ahmad Sakor,
  • Kuldeep Singh,
  • Maria-Esther Vidal

摘要

In this paper, we introduce BioLinkerAI, a neuro-symbolic approach tailored for biomedical entity linking. Traditional domain-specific entity linking approaches necessitate substantial labeled-training datasets and present challenges when adapting to each new dataset or setting. BioLinkerAI integrates symbolic methodologies with sub-symbolic models to mitigate the constraints of limited training data availability. The symbolic component utilizes a rule-based entity extraction mechanism, underpinned by an extensive set of linguistic and domain-specific rules. Concurrently, the sub-symbolic component employs a Large Language Model (LLM) to achieve precise candidate disambiguation. This mechanism enhances entity linking accuracy, especially when a single entity in a knowledge base such as UMLS aligns with multiple terms, leveraging the contextual intelligence encapsulated within the LLM’s embeddings. Empirical evaluations conducted on a range of biomedical benchmarks demonstrate the superior performance of BioLinkerAI. Notably, it surpasses existing benchmarks in entity linking accuracy (e.g., achieving an improvement from 65.4 (best baseline) to 78.5 (our model) on unseen data accuracy, representing the most stringent evaluation paradigm). Additionally, BioLinkerAI consistently performs adeptly on both structured sentences and individual keywords. To facilitate broader utilization, the source code, datasets, and a public API are available ( https://github.com/SDM-TIB/BioLinkerAI ).