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

TL;DR
Know2BIO is a comprehensive, multi-modal benchmark for biomedical knowledge graphs that integrates diverse data sources, supports continuous updates, and facilitates evaluation of representation models in the biomedical domain.
Contribution
It introduces Know2BIO, a large-scale, heterogeneous biomedical knowledge graph benchmark with multi-modal data and automated updating capabilities.
Findings
Effective evaluation of KG representation models demonstrated
Supports multi-modal data integration for biomedical applications
Enables continuous updates to reflect latest scientific knowledge
Abstract
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…
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Bioinformatics and Genomic Networks · Machine Learning in Bioinformatics
