Incorporating Relation Path and Entity Neighborhood Information for Knowledge Graph Completion Method

doi:10.3778/j.issn.1002-8331.2303-0369

Abstract

Abstract: Knowledge graph provides the underlying technical support for many AI applications, including e-commerce, smart navigation, healthcare, social media, and more. However, the existing knowledge graph is usually sparse, and a large amount of hidden knowledge has not been mined, so how to complete the knowledge map has become an urgent problem to be solved. Most of the existing methods process entity neighborhood information or relationship paths independently, ignoring the importance of entity neighborhood information to the relationship path exploration process. Therefore, a knowledge graph completion method （RPEN-KGC） is proposed to fuse relational path and entity neighborhood information. RPEN-KGC consists of a sampler and an inference. The sampler provides an expert path for the inferent by randomly walking between pairs of entities, and at the same time restricts the direction of random walk with the entity neighborhood similarity comparison mechanism to enrich the expert path. By extracting the semantic features in the relationship path, the inferent can infer more diverse relationship paths in the semantic space. Experimental verification is carried out on the publicly available NELL-995 and FB15K-237 datasets by link prediction task. The experimental results show that RPEN-KGC is improved compared with the baseline method in most indicators, indicating that RPEN-KGC can effectively predict the missing knowledge in the knowledge graph.

Key words: knowledge graph, knowledge graph completion, generative adversarial network, multi-hop reasoning

摘要： 知识图谱为许多人工智能应用提供了底层的技术支持，包括电子商务、智能导航、医疗保健、社交媒体等领域。但现有的知识图谱通常是不完整的，大量的知识隐含在其中，因此如何将知识图谱补全完整成为亟需解决的问题。现有方法大多是独立处理实体邻域信息或关系路径，忽略了实体邻域信息对关系路径探索过程的重要性。为此，提出了一种融合关系路径与实体邻域信息的知识图谱补全方法（RPEN-KGC）。RPEN-KGC由采样器和推理器构成，采样器通过在实体对之间随机游走，为推理器提供可靠的推理策略。同时采样器利用实体邻域相似性对比机制约束随机游走的方向，有效提高采样的效率，并且使推理策略更加丰富。推理器通过提取关系路径的语义特征，在语义空间中推理出更加多样化的关系路径。在公开的NELL-995和FB15K-237数据集中通过链接预测任务进行实验验证，结果表明，RPEN-KGC在多数指标上相较于基准方法均有一定的提升，说明RPEN-KGC能够有效预测知识图谱中缺失的知识。

关键词: 知识图谱, 知识图谱补全, 生成对抗网络, 多跳推理

ZHAI Sheping, KANG Xinnian, LI Fangyi, YANG Rui. Incorporating Relation Path and Entity Neighborhood Information for Knowledge Graph Completion Method[J]. Computer Engineering and Applications, 2024, 60(13): 136-142.

翟社平, 亢鑫年, 李方怡, 杨锐. 融合关系路径与实体邻域信息的知识图谱补全方法[J]. 计算机工程与应用, 2024, 60(13): 136-142.

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