分析生物医学知识图谱拓扑结构如何影响补全模型性能
The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
- 系统研究生物医学KG的拓扑特性与模型表现的关系
- 发现特定图结构特征显著提升补全任务准确率
- 开源模型预测结果与分析工具，供社区复用
知识图谱补全在药物重定位、药物靶点识别等生物医学研究任务中日益重要。尽管已有多种数据集和知识图嵌入模型被提出，但关于哪些数据属性及建模选择对特定任务有效仍知之甚少。同时，尽管知识图嵌入模型的理论性质已被充分理解，其实际应用效果在该领域仍存争议。本文全面分析了公开可用的生物医学知识图谱的拓扑特性，并建立其与真实任务中模型准确率之间的关联。通过发布所有模型预测结果及一套新的分析工具，本文邀请社区在此基础上进一步深化对这些关键应用的理解。
原文摘要 · Abstract (English)
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.
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