Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical Knowledge Graphs
This provides a benchmark for knowledge graph representation learning in the biomedical domain, addressing scalability and update issues, but it is incremental as it builds on existing KG frameworks.
The authors tackled the challenge of assembling and updating biomedical knowledge graphs from diverse sources by proposing Know2BIO, a comprehensive benchmark that integrates data from 30 sources with ~219,000 nodes and ~6,200,000 edges, enabling automated updates and multi-modal data integration.
Knowledge graphs (KGs) have emerged as a powerful framework for representing and integrating complex biomedical information. However, assembling KGs from diverse sources remains a significant challenge in several aspects, including entity alignment, scalability, and the need for continuous updates to keep pace with scientific advancements. Moreover, the representative power of KGs is often limited by the scarcity of multi-modal data integration. To overcome these challenges, we propose Know2BIO, a general-purpose heterogeneous KG benchmark for the biomedical domain. Know2BIO integrates data from 30 diverse sources, capturing intricate relationships across 11 biomedical categories. It currently consists of ~219,000 nodes and ~6,200,000 edges. Know2BIO is capable of user-directed automated updating to reflect the latest knowledge in biomedical science. Furthermore, Know2BIO is accompanied by multi-modal data: node features including text descriptions, protein and compound sequences and structures, enabling the utilization of emerging natural language processing methods and multi-modal data integration strategies. We evaluate KG representation models on Know2BIO, demonstrating its effectiveness as a benchmark for KG representation learning in the biomedical field. Data and source code of Know2BIO are available at https://github.com/Yijia-Xiao/Know2BIO/.