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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

翟社平 亢鑫年 李方怡 杨锐

计算机工程与应用2024,Vol.60Issue(13):136-142,7.
计算机工程与应用2024,Vol.60Issue(13):136-142,7.DOI:10.3778/j.issn.1002-8331.2303-0369

融合关系路径与实体邻域信息的知识图谱补全方法

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

翟社平 1亢鑫年 2李方怡 2杨锐2

作者信息

  • 1. 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
  • 2. 西安邮电大学 计算机学院,西安 710121
  • 折叠

摘要

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 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.

关键词

知识图谱/知识图谱补全/生成对抗网络/多跳推理

Key words

knowledge graph/knowledge graph completion/generative adversarial network/multi-hop reasoning

分类

信息技术与安全科学

引用本文复制引用

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):136-142,7.

基金项目

国家自然科学基金(61373116) (61373116)

工业和信息化部通信软科学项目(2018-R-26) (2018-R-26)

陕西省教育厅科学研究计划项目(18JK0697) (18JK0697)

陕西省社会科学基金(2016N008) (2016N008)

陕西省重点研发计划项目(2022GY-038) (2022GY-038)

西安市社会科学规划基金(17X63) (17X63)

西安邮电大学研究生创新基金(CXJJYL2021041) (CXJJYL2021041)

陕西省大学生创新创业训练计划项目省级项目(202211664053) (202211664053)

陕西省大学生创新创业训练计划项目省级项目(202211664086). (202211664086)

计算机工程与应用

OA北大核心CSTPCD

1002-8331

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