BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment

• URL: http://arxiv.org/abs/2509.07588v1
• Date: Tue, 09 Sep 2025 10:59:47 GMT
• Title: BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
• Authors: Andrey Sakhovskiy, Elena Tutubalina,
• Abstract summary: BALI (Biomedical Knowledge Graph and Language Model Alignment) is a novel joint LM and KG pre-training method.<n>It augments an LM with external knowledge by the simultaneous learning of a dedicated KG encoder and aligning the representations of both the LM and the graph.<n>Our empirical findings indicate that implementing our method on several leading biomedical LMs, such as PubMedBERT and BioLinkBERT, improves their performance on a range of language understanding tasks.
• Score: 12.413588969346627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, there has been substantial progress in using pretrained Language Models (LMs) on a range of tasks aimed at improving the understanding of biomedical texts. Nonetheless, existing biomedical LLMs show limited comprehension of complex, domain-specific concept structures and the factual information encoded in biomedical Knowledge Graphs (KGs). In this work, we propose BALI (Biomedical Knowledge Graph and Language Model Alignment), a novel joint LM and KG pre-training method that augments an LM with external knowledge by the simultaneous learning of a dedicated KG encoder and aligning the representations of both the LM and the graph. For a given textual sequence, we link biomedical concept mentions to the Unified Medical Language System (UMLS) KG and utilize local KG subgraphs as cross-modal positive samples for these mentions. Our empirical findings indicate that implementing our method on several leading biomedical LMs, such as PubMedBERT and BioLinkBERT, improves their performance on a range of language understanding tasks and the quality of entity representations, even with minimal pre-training on a small alignment dataset sourced from PubMed scientific abstracts.

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