Xie Zhiwen, Zhu Runjie, Liu Jin, Zhou Guangyou, Huang Jimmy Xiangji, Cui Xiaohui
School of Computer Science, Wuhan University, Wuhan 430072, China.
Lassonde School of Engineering, York University, Toronto, Canada.
Inf Sci (N Y). 2022 Aug;608:1557-1571. doi: 10.1016/j.ins.2022.07.031. Epub 2022 Jul 14.
In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task.
为应对抗击新冠疫情，机器学习和人工智能领域的研究人员基于现有的新冠数据集构建了一些医学知识图谱（KG），然而，这些知识图谱包含大量不完整或缺失的语义关系。在本文中，我们聚焦于知识图谱嵌入（KGE）任务，它是推断缺失关系的重要解决方案。过去，已经发表了一系列具有不同评分函数的知识图谱嵌入模型来学习实体和关系嵌入。然而，在处理异构、复杂且不完整的新冠医学数据时，这些模型存在相同的问题，即除了关系三元组外，很少考虑知识图谱的重要特征，如属性特征。为解决上述问题，我们针对新冠知识图谱嵌入任务提出了一种图特征收集网络（GFCNet），该网络同时考虑了知识图谱中的邻居特征和属性特征。在新冠药物知识图谱数据集上进行的大量实验显示出了有前景的结果，并证明了我们提出的模型的有效性和效率。此外，我们还阐述了深化新冠知识图谱嵌入任务研究的未来方向。
