论文标题
将逻辑规则整合到神经多跳的推理中，以重新使用药物
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
论文作者
论文摘要
生物医学数据的图结构与典型知识图基准任务的图形结构不同。生物医学数据的特定属性是长期依赖性的存在，可以通过描述为逻辑规则的模式来捕获。我们提出了一种新颖的方法，将这些规则与使用强化学习的神经多跳跃推理方法相结合。我们通过将此任务作为链接预测问题制定来进行药物重新利用的现实世界任务进行实证研究。我们将方法应用于生物医学知识图Hetionet，并表明我们的方法的表现优于几种基线方法。
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link prediction problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approach outperforms several baseline methods.
