Know2BIO: A Comprehensive Dual-View Benchmark for Evolving Biomedical
  Knowledge Graphs
Yijia Xiao Dylan Steinecke Alexander Russell Pelletier Yushi Bai Peipei Ping Wei Wang
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/.