计算机工程2026，Vol.52Issue(4)：122-130,9.DOI:10.19678/j.issn.1000-3428.0069984
基于关系约束对比学习的常识知识图谱补全方法
Commonsense Knowledge Graph Completion Method Based on Relation-Constrained Contrastive Learning
摘要
Abstract
Knowledge graph completion aims to address the problems of knowledge deficiency and incompleteness,by predicting missing entities or relationships in a knowledge graph.Compared to traditional knowledge graphs,commonsense knowledge graphs are typically sparser,making them insufficient for representing entities solely based on structural information.Therefore,some studies enrich commonsense knowledge graphs by utilizing semantic representations in addition to structural information.However,these methods typically focus only on the semantic representation of individual entities and ignore the semantic associations between entity sets.To address this issue,this study proposes a new method called relation-constrained contrastive learning for common-sense knowledge graph completion.First,the method uses relations to divide entities into different sets and selects positive and negative sample pairs from these sets for contrastive learning,to obtain the basic representations of the entities.It further learns comprehensive entity representations by constraining the similarity between individual entity semantic representations and the central representations of the sets to which the entities belong.The completion task is performed based on these comprehensive representations.Experiments on two public datasets show that the proposed model outperforms baseline models.Compared to the second-best model,CPNC,the proposed model improves the Mean Reciprocal Rank(MRR)by 1.09 and 2.48 percentage points and Hits@1 by 1.02 and 1.55 percentage points on the CN-100K and ATOMIC datasets,respectively.关键词
常识知识图谱/知识图谱补全/对比学习/关系约束/语义表征Key words
commonsense knowledge graph/knowledge graph completion/contrastive learning/relation constraint/semantic representation分类
信息技术与安全科学引用本文复制引用
和红光,线岩团,相艳..基于关系约束对比学习的常识知识图谱补全方法[J].计算机工程,2026,52(4):122-130,9.基金项目
国家自然科学基金重点项目(U23A20388,U21B2027) （U23A20388,U21B2027）
云南省青年学术和技术带头人后备人才计划(202305AC160063) （202305AC160063）
云南省重大科技专项计划项目(202202AD080004,202202AD080003). （202202AD080004,202202AD080003）
