Abstract:

Relationship prediction in knowledge graph aims to identify and infer new relationships from existing data, and provides knowledge services for many downstream tasks. At present, many researches solve the link prediction problem between entities by mapping entities and relations into a vector space or searching the paths between entities. These methods only consider the influence of single path or first-order information but ignore more complex relation information between entities. Therefore, this paper proposes a novel link prediction method based on subgraph reasoning in knowledge graph, uses the subgraph structure to obtain the entity pair neighborhood structure information, combines the advantages of representation learning and path reasoning, and realizes the relationship prediction between entities. This paper first extends the paths between entities to subgraphs, constructs node subgraph and relationship subgraph from entity level and relationship level respectively, then combines the graph embedding representation with the graph neural network to calculate the subgraph features, to get richer entity characteristics and relationship characteristics. Finally, this paper calculates the neighborhood structure information of entity pairs from the subgraph structure to conduct link prediction between entities. Experimental results demons-trate that the proposed approach outperforms other reasoning-based link prediction methods on two benchmark datasets.

Key words: knowledge graph, link prediction, subgraph construction, graph neural network

摘要：

知识图谱中的关系推理旨在从现有数据中识别和推断出新的关系，为许多下游任务提供知识服务。当前的许多研究工作主要将实体与关系映射到向量空间中或对实体之间的路径进行搜索来解决关系推理问题。这些方法都只考虑了单一路径或一阶信息对关系推理的影响，忽视了广泛存在于实体之间的更复杂的关系信息。提出了一种新颖的基于子图的知识图谱关系推理方法，结合表示学习与路径推理的优势，使用具有丰富信息的子图结构获取实体对的邻域结构信息，实现实体之间的关系预测。首先将实体对之间的路径扩展为子图，分别从实体层面和关系层面出发，构建节点子图和关系子图;再结合图嵌入表示与图神经网络计算子图的高阶特征，从而获得更丰富的实体关系特征;最后从子图高阶特征中获取实体对的邻域结构信息，实现实体之间的关系预测。实验结果表明，在两个基准数据集上，该方法优于现有的其他基于推理的关系预测方法。

关键词: 知识图谱, 连接预测, 子图构建, 图神经网络