Towards Linking Graph Topology to Model Performance for Biomedical Knowledge Graph Completion

Alberto Cattaneo, Thomas Martynec, Stephen Bonner, Carlo Luschi, Daniel Justus

Keywords: Machine Learning, Knowledge Graphs, Knowledge Graph Completion, Graph Topology, Drug Discovery, Biomedical Research

TL;DR: This study links topological properties of biomedical Knowledge Graphs to the accuracy observed in Knowledge Graph Completion tasks and provides new tools to study this connection.

Abstract:

Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models has been proposed over the years. However, little is known about the properties that render a dataset useful for a given task and, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial.
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 applications. 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.

Submission Number: 2