The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
This work addresses the gap in understanding dataset properties and modeling choices for biomedical knowledge graph completion, which is incremental but crucial for improving applications in drug discovery.
The study investigated how the topological properties of biomedical knowledge graphs affect the accuracy of knowledge graph completion models in real-world tasks like drug repurposing, providing analysis tools and model predictions to aid further research.
Knowledge Graph Completion has been increasingly adopted as a useful method for helping address several tasks in biomedical research, such as drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models have been proposed over the years. However, little is known about the properties that render a dataset, and associated modelling choices, useful for a given task. Moreover, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. In this work, we conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world tasks. By releasing all model predictions and a new suite of analysis tools we invite the community to build upon our work and continue improving the understanding of these crucial applications.