Paper Type
Complete
Paper Number
PACIS2026-1786
Description
Biomedical knowledge graphs (KGs) have become fundamental infrastructures for biomedical research and clinical decision support. However, large-scale biomedical KGs are predominantly constructed through automated extraction from scientific literature, which often results in incomplete, outdated, or noisy relational information. Furthermore, many meaningful associations may not appear explicitly as extracted triplets due to extraction limitations. To address this limitation, we propose a New Relation Classification (NRC) framework for predicting unseen relations in biomedical knowledge graphs. The proposed method consists of two stages: (1) a knowledge graph embedding (KGE) training stage, and (2) a triplet classification training stage to determine whether a biomedical triplet forms a valid specific new relation. Using SemMedDB and 879 domain-expert-annotated biomedical triplets, we evaluate the proposed method. Our findings suggest that the proposed NRC framework provides a promising direction for dynamically extending biomedical knowledge graphs and improving the reliability of downstream biomedical applications.
Recommended Citation
Tsai, Pei Yuan; Wei, Chih-Ping; Wu, Hung-Ta; Li, Jih Jane; and Wang, Yu-Ming, "From Known Relations to New One: Learning to Classify Triplets of Unseen Relations from A Biomedical Knowledge Graph" (2026). PACIS 2026 Proceedings. 17.
https://aisel.aisnet.org/pacis2026/ishealthcare/ishealthcare/17
From Known Relations to New One: Learning to Classify Triplets of Unseen Relations from A Biomedical Knowledge Graph
Biomedical knowledge graphs (KGs) have become fundamental infrastructures for biomedical research and clinical decision support. However, large-scale biomedical KGs are predominantly constructed through automated extraction from scientific literature, which often results in incomplete, outdated, or noisy relational information. Furthermore, many meaningful associations may not appear explicitly as extracted triplets due to extraction limitations. To address this limitation, we propose a New Relation Classification (NRC) framework for predicting unseen relations in biomedical knowledge graphs. The proposed method consists of two stages: (1) a knowledge graph embedding (KGE) training stage, and (2) a triplet classification training stage to determine whether a biomedical triplet forms a valid specific new relation. Using SemMedDB and 879 domain-expert-annotated biomedical triplets, we evaluate the proposed method. Our findings suggest that the proposed NRC framework provides a promising direction for dynamically extending biomedical knowledge graphs and improving the reliability of downstream biomedical applications.
Comments
14-Healthcare