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融合关系路径与实体邻域信息的知识图谱补全方法

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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.
Incorporating Relation Path and Entity Neighborhood Information for Knowledge Graph Completion Method
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 indepen-dently,ignoring the importance of entity neighborhood information to the relationship path exploration process.There-fore,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 rela-tionship path,the inferent can infer more diverse relationship paths in the semantic space.Experimental verification is car-ried 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.

knowledge graphknowledge graph completiongenerative adversarial networkmulti-hop reasoning

翟社平、亢鑫年、李方怡、杨锐

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西安邮电大学 计算机学院,西安 710121

西安邮电大学 陕西省网络数据分析与智能处理重点实验室,西安 710121

知识图谱 知识图谱补全 生成对抗网络 多跳推理

国家自然科学基金工信部通信软科学研究计划陕西省教育厅科研项目陕西省社会科学基金陕西省重点研发计划西安市社会科学基金规划项目西安邮电大学研究生创新基金陕西省大学生创新创业训练计划省级项目陕西省大学生创新创业训练计划省级项目

613731162018-R-2618JK06972016N0082022GY-03817X63CXJJYL2021041202211664053202211664086

2024

10.3778/j.issn.1002-8331.2303-0369

计算机工程与应用
华北计算技术研究所

计算机工程与应用

CSTPCD北大核心
影响因子：0.683
ISSN：1002-8331
年,卷(期)：2024.60(13)