GRABLI: Cross-Modal Knowledge Graph Alignment for Biomedical Language Models

Andrey Sakhovskiy, Elena Tutubalina

Keywords: Biomedical Knowledge Graph, Biomedical Language model, Representation Learning, Contrastive Learning

TL;DR: The paper proposes a novel flexible for alignment between language model and knowledge graph representations

Abstract:

Pre-trained Language Models (LMs) have given a significant performance growth in a variety of language-related texts in biomedical domain. However, existing biomedical LLMs demonstrate a limited understanding of complex, domain-specific concept structure and the factual information stored in biomedical Knowledge Graphs (KGs). We propose \textbf{GRABLI} (Knowledge \textbf{Gra}ph and \textbf{B}iomedical Language Model A\textbf{li}gnment), a novel pre-training method that enriches an LM with external knowledge by simultaneously learning a separate KG encoder and aligning LM and graph representations. Given a textual sequence, we normalize biomedical concept mentions to the Unified Medical Language System (UMLS) KG and use the local KG subgraphs as cross-modal positive samples for mentioned concepts. Our empirical results demonstrate that applying our proposed method to various state-of-the-art biomedical LMs including PubMedBERT and BioLinkBERT, enhances their performance on diverse language understanding tasks, even after brief pre-training on a small alignment dataset derived from PubMed scientific abstracts.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 11940