Asada Masaki, Gunasekaran Nallappan, Miwa Makoto, Sasaki Yutaka
Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya, Japan.
Front Res Metr Anal. 2021 Jul 1;6:670206. doi: 10.3389/frma.2021.670206. eCollection 2021.
We deal with a heterogeneous pharmaceutical knowledge-graph containing textual information built from several databases. The knowledge graph is a heterogeneous graph that includes a wide variety of concepts and attributes, some of which are provided in the form of textual pieces of information which have not been targeted in the conventional graph completion tasks. To investigate the utility of textual information for knowledge graph completion, we generate embeddings from textual descriptions given to heterogeneous items, such as drugs and proteins, while learning knowledge graph embeddings. We evaluate the obtained graph embeddings on the link prediction task for knowledge graph completion, which can be used for drug discovery and repurposing. We also compare the results with existing methods and discuss the utility of the textual information.
我们处理的是一个异构的药学知识图谱，它包含从多个数据库构建的文本信息。该知识图谱是一个异构图，包含各种各样的概念和属性，其中一些是以文本信息的形式提供的，而这些文本信息在传统的图补全任务中并未作为目标。为了研究文本信息对知识图谱补全的效用，我们在学习知识图谱嵌入的同时，从给予异构项目（如药物和蛋白质）的文本描述中生成嵌入。我们在用于知识图谱补全的链接预测任务上评估所获得的图嵌入，该任务可用于药物发现和药物重新利用。我们还将结果与现有方法进行比较，并讨论文本信息的效用。
