Papers › The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
Alberto Cattaneo, Stephen Bonner, Thomas Martynec, Carlo Luschi, Ian P Barrett, Daniel Justus
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.
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