Joint Relational Patterns and Analogy Transfer Knowledge Graph Completion Method
宋宝燕 1刘杭生 1单晓欢 1李素 1陈泽1
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作者信息
1. 辽宁大学信息学部 沈阳 110036
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摘要
近年来,知识图谱嵌入(Knowledge Graph Embedding,KGE)作为一种主流方法在知识图谱补全任务中已取得显著效果.然而,现有KGE方法仅在数据层考虑三元组信息,忽略了不同三元组间在逻辑层存在的关系模式语义,导致现有方法仍存在一定性能缺陷.针对上述问题,提出一种融合关系模式和类比迁移的知识图谱补全方法(Fusing Relational-pattern and Ana-logy Transfer,RpAT).首先,在逻辑层,根据实体关系的语义层次结构,细分为不同的关系模式;其次,在数据层,提出一种模式类比对象生成方法,该方法利用关系模式性质生成目标三元组相似类比对象,依据类比对象对缺失信息进行迁移;最后,提出一种融合了原始知识图谱嵌入模型的推理能力与类比迁移能力的综合性评分函数,以提升图谱补全性能.实验结果表明,在FB15k-237和 WN18RR数据集上,相较于其他基线模型,RpAT方法的MRR值分别提升了 15.5％和1.8％,验证了在知识图谱补全任务中的有效性.
Abstract
In recent years,knowledge graph embedding(KGE)has emerged as a mainstream approach and achieved significant re-sults in the task of knowledge graph completion.However,existing KGE methods only consider the information of triplets at the data level,neglecting the semantic relational patterns that exist between different triplets at the logical level,leading to certain performance deficiencies in current methods.To address this issue,a knowledge graph completion method(RpAT)that integrates relational patterns and analogy transfer is proposed.Firstly,at the logical level,different relational patterns are refined according to the semantic hierarchy of entity relationships.Secondly,at the data level,a method for generating pattern analogy objects is proposed,which utilizes the properties of relational patterns to generate similar analogy objects for target triplets and transfers missing information based on these analogy objects.Finally,a comprehensive scoring function that integrates the reasoning capa-bilities of the original knowledge graph embedding model and the analogy transfer capabilities is proposed to enhance the per-formance of graph completion.Experimental results show that,compared to other baseline models,the RpAT method pimproves the MRR values by 15.5％and 1.8％on the FB15k-237 and WN18RR datasets,respectively,demonstrating its effectiveness in the task of knowledge graph completion.
关键词
知识图谱/知识图谱补全/关系模式/类比对象/类比迁移
Key words
Knowledge graph/Knowledge graph completion/Relational patterns/Analogy objects/Analogy transfer
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