A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.

ABSTRACT: To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient information retrieval and automated knowledge discovery (AKD). However, transforming unstructured scientific literature into KGs remains a significant challenge, with previous methods unable to achieve human-level accuracy. In this study, we utilized an information extraction pipeline that won first place in the LitCoin NLP Challenge (2022) to construct a large-scale KG named iKraph using all PubMed abstracts. The extracted information matches human expert annotations and significantly exceeds the content of manually curated public databases. To enhance the KG's comprehensiveness, w

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10760044 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature