The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models

2024-09-06

arXiv.org

AI Summary

This study investigates how topological properties of biomedical knowledge graphs (KGs) influence link prediction performance. We systematically analyze structural characteristics—including sparsity, degree distribution, clustering coefficient, and relation symmetry—across benchmark datasets (e.g., DrugBank, Hetionet), and evaluate their impact on KG completion using representative embedding models (TransE, RotatE, ComplEx) via controlled ablation studies. Our key contribution is the first empirical quantification of associations between graph-structural metrics and link prediction accuracy (measured by MRR and mean rank), achieving an R² of 0.73. We identify local clustering coefficient and relation symmetry as the most predictive topological factors. To ensure reproducibility and facilitate structural attribution analysis, we publicly release all prediction results and a dedicated analytical toolkit. This work establishes interpretable, topology-aware design principles for biomedical KG modeling, bridging structural graph theory with practical knowledge representation tasks.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Investigates how graph topology affects biomedical knowledge graph completion performance
• Examines link between topological properties and real-world task accuracy
• Addresses lack of understanding about dataset properties for biomedical tasks

Innovation

• Analyzing biomedical knowledge graph topology
• Linking graph properties to task accuracy
• Releasing predictive models and analysis tools