袁培, 王儒敬. 基于表示学习的多关系型知识图谱推理算法[J]. 微电子学与计算机, 2022, 39(4): 75-82. DOI: 10.19304/J.ISSN1000-7180.2021.1089
引用本文: 袁培, 王儒敬. 基于表示学习的多关系型知识图谱推理算法[J]. 微电子学与计算机, 2022, 39(4): 75-82. DOI: 10.19304/J.ISSN1000-7180.2021.1089
YUAN Pei, WANG Rujing. Multi-relational knowledge graph reasoning algorithm based on representation learning[J]. Microelectronics & Computer, 2022, 39(4): 75-82. DOI: 10.19304/J.ISSN1000-7180.2021.1089
Citation: YUAN Pei, WANG Rujing. Multi-relational knowledge graph reasoning algorithm based on representation learning[J]. Microelectronics & Computer, 2022, 39(4): 75-82. DOI: 10.19304/J.ISSN1000-7180.2021.1089

基于表示学习的多关系型知识图谱推理算法

Multi-relational knowledge graph reasoning algorithm based on representation learning

  • 摘要: 目前知识图谱的推理方法中,表示学习尤其是基于翻译的TransE系列算法取得了优异表现.其相关论文大都关注实体推理,然而关系推理作为知识图谱补全的关键技术值得受到关注与研究.与此同时,在规模不断扩大、知识来源更加多样化的知识图谱中,关系种类繁多且类型复杂,单个关系在全体三元组中的出现频率进一步降低,这为关系推理增加了难度.因此针对多关系型知识图谱,基于TransE模型并侧重知识图谱三元组中关系的推理,提出一种新的关系建模方法,通过调整向量空间中实体向量与关系向量间的组织结构,缓解多映射属性关系中不同种类的关系争抢同一向量的问题.然后又与其他方法结合,使新的模型在实体推理上具备可行性.通过在公开的FB15k数据集以及自行从网络中抽取得到的中文数据集上展开的知识推理实验,从关系推理准确率与实体推理准确率等指标与相似方法进行对比,均取得了良好的表现,成功验证了算法的有效性与先进性.

     

    Abstract: In the current reasoning methods of knowledge graph, representation learning, especially the TransE series of algorithms based on translation, has achieved excellent performance. Most of the related papers focus on entity reasoning, however, relational reasoning as a key technology for knowledge graph completion deserves attention and research. At the same time, in the knowledge graph with ever-expanding scale and more diversified sources of knowledge, there are many more and complex types of relations, and the frequency of one kind of relations in all triples is further reduced, which increases the difficulty of relational reasoning.Therefore, for the multi-relational knowledge graph, based on the TransE model and focusing on the relational reasoning, a new relationship modeling method is proposed, which can alleviate the problem of multiple relations competing for the same vector in the multi-mapping attribute relations. Then combined with other methods to make the new model feasible in entity reasoning. Through the knowledge reasoning experiments carried out on the public FB15k data set and the Chinese data set extracted from the network, comparing the accuracy of relational reasoning and entity reasoning with similar methods, good results have been achieved and it successfully verify the effectiveness and advancement of the proposed algorithm.

     

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