Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs

• URL: http://arxiv.org/abs/2501.01644v1
• Date: Fri, 03 Jan 2025 05:29:12 GMT
• Title: Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
• Authors: Tien Dang, Viet Thanh Duy Nguyen, Minh Tuan Le, Truong-Son Hy,
• Abstract summary: PrimeKG++ is an enriched knowledge graph incorporating multimodal data. Our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes.
• Score: 2.006175707670159
• License:
• Abstract: Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential novel drug-disease relations. We introduce a novel multimodal approach that unifies embeddings from specialized Language Models (LMs) with Graph Contrastive Learning (GCL) to enhance intra-entity relationships while employing a Knowledge Graph Embedding (KGE) model to capture inter-entity relationships for effective link prediction. To address limitations in existing BKGs, we present PrimeKG++, an enriched knowledge graph incorporating multimodal data, including biological sequences and textual descriptions for each entity type. By combining semantic and relational information in a unified representation, our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes. Experimental results on PrimeKG++ and the DrugBank drug-target interaction dataset demonstrate the effectiveness and robustness of our method across diverse biomedical datasets. Our source code, pre-trained models, and data are publicly available at https://github.com/HySonLab/BioMedKG

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