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数据分析与知识发现  2023, Vol. 7 Issue (2): 15-25     https://doi.org/10.11925/infotech.2096-3467.2022.1027
  专题 本期目录 | 过刊浏览 | 高级检索 |
基于实体与关系融合的知识图谱补全模型研究*
张贞港,余传明()
中南财经政法大学信息与安全工程学院 武汉 430073
Knowledge Graph Completion Model Based on Entity and Relation Fusion
Zhang Zhengang,Yu Chuanming()
School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
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摘要 

目的】 将实体与关系融合,通过加权图卷积神经网络和关系归纳机制,聚合知识图谱的全局信息,增强知识图谱表示质量,提升其在知识图谱补全任务的效果。【方法】 提出一种新的用于知识图谱补全任务的端到端学习模型,该模型由邻居信息聚合模块、实体关系融合模块、交互模块和预测模块组成。邻居信息聚合模块聚合实体的邻居信息以丰富实体表示;实体关系融合模块利用实体之间的关系融合实体表示与关系表示;交互模块通过构建核心张量增强与实体和关系表示的交互;预测模块获取最终的预测结果。将所提模型应用到FB15K237、WN18RR、Kinship和UMLS4个数据集上,开展实证研究。【结果】 与传统的知识图谱补全模型相比,所提模型的Hits@1指标在FB15K237、WN18RR、Kinship和UMLS这4个数据集上分别提升4.1、3.9、17.8和5.3个百分点。【局限】 尚未探究知识图谱补全模型迁移到信息检索、推荐系统等任务上的效果。【结论】 通过加权图卷积网络,关系归纳机制以及对比学习损失能够显著提升知识图谱补全任务的效果。本研究对于补全知识图谱中的缺失信息,提升知识图谱在信息检索、自动问答等领域的应用效果具有重要参考意义。

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张贞港
余传明
关键词 知识图谱知识图谱补全深度学习图卷积网络    
Abstract

[Objective] This study aggregates the global information of knowledge graph through a weighted graph convolutional neural network and a relational induction mechanism, aiming to enhance the quality of the knowledge graph representation and completion. [Methods] We proposed an end-to-end learning model for the knowledge graph completion task, which included a neighborhood information aggregation module, an entity relationship fusion module, an interaction module, as well as a prediction module. This new model aggregates the neighborhood information of entities to enrich their representations. It also enhances the interaction between entities and relationship representations with a core tensor. [Results] We examined the new model with the FB15K237, WN18RR, Kinship, and UMLS datasets. Compared with traditional knowledge graph completion models, the Hits@1 indicators of the proposed model increased by 4.1%, 3.9%, 17.8%, and 5.3% on the four datasets, respectively. [Limitations] We did not explore the performance of our new model on information retrieval and recommendation systems. [Conclusions] The proposed model significantly improves the effectiveness of the knowledge graph completion, which helps us identify missing information in knowledge graphs and may benefit information retrieval and automatic Q&A applications.

Key wordsKnowledge Graph    Knowledge Graph Completion    Deep Learning    Graph Convolutional Networks
收稿日期: 2022-09-28      出版日期: 2023-03-28
ZTFLH:  G353  
基金资助:*国家自然科学基金面上项目(71974202);中南财经政法大学中央高校基本科研业务费专项资金资助项目的研究成果之一(202311401)
通讯作者: 余传明,ORCID:0000-0001-7099-0853,E-mail:yucm@zuel.edu.cn。   
引用本文:   
张贞港, 余传明. 基于实体与关系融合的知识图谱补全模型研究*[J]. 数据分析与知识发现, 2023, 7(2): 15-25.
Zhang Zhengang, Yu Chuanming. Knowledge Graph Completion Model Based on Entity and Relation Fusion. Data Analysis and Knowledge Discovery, 2023, 7(2): 15-25.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.1027      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I2/15
Fig.1  ERF-KGC模型框架
数据集 实体数量 关系数量 训练集 验证集 测试集
FB15K237 14 541 237 272 115 17 535 20 466
WN18RR 40 943 11 86 835 3 034 3 134
Kinship 104 25 8 544 1 068 1 074
UMLS 135 46 5 216 652 661
Table 1  数据集信息
模型 FB15K237 WN18RR
MRR/% Hits@1/% Hits@3/% Hits@10/% MRR/% Hits@1/% Hits@3/% Hits@10/%
TransE 28.6 19.0 32.6 47.3 22.1 4.6 35.4 50.4
TransH 29.2 19.1 33.3 49.3 22.6 4.9 36.8 50.3
TransR 31.5 21.6 35.4 51.0 23.5 4.7 39.8 50.7
TransD 28.4 18.1 32.5 48.7 21.9 3.8 36.7 50.8
DistMult 24.1 15.5 26.3 41.9 43.0 39.0 44.0 49.0
ComplEx 24.7 15.8 27.5 42.8 44.0 41.0 46.0 51.0
TuckER 35.8 26.6 39.4 54.4 47.0 44.3 48.2 52.6
ConvE 31.2 22.5 34.1 49.7 43.0 40.0 44.0 52.0
ConvR 35.0 26.1 38.5 52.8 47.5 44.3 48.9 53.7
HypER 34.1 25.2 37.6 52.0 46.5 43.6 47.7 52.2
AcrE 35.8 26.6 39.3 54.5 45.9 42.2 47.3 53.2
R-GCN 16.4 10.0 18.1 30.0 12.3 8.0 13.7 20.7
SACN 35.0 26.0 39.0 54.0 47.0 43.0 48.0 54.0
CompGCN 35.5 26.4 39.0 53.5 47.9 44.3 49.4 54.6
ComplexGCN 33.8 24.5 37.1 52.4 45.5 42.3 46.8 51.6
ERF-KGC 37.2 30.7 42.8 55.7 49.5 48.2 54.0 63.2
Table 2  ERF-KGC模型与基线模型在FB15K237和WN18RR数据集上的效果
模型 Kinship UMLS
MRR/% Hits@1/% Hits@3/% Hits@10/% MRR/% Hits@1/% Hits@3/% Hits@10/%
TransE 30.9 9.0 64.3 84.1 98.9
DistMult 51.6 36.7 58.1 86.7 84.6
ComplEx 82.3 73.3 89.9 97.1 96.7
ConvE 83.3 73.8 91.7 98.1 94.0 92.0 96.0 99.0
R-GCN 10.9 3.0 8.8 23.9
ERF-KGC 88.5 91.6 96.5 98.6 93.0 97.3 98.9 99.6
Table 3  ERF-KGC模型与基线模型在Kinship和UMLS数据集上的效果
模型 MRR/% Hits@1/% Hits@3/% Hits@10/%
ERF-KGC 37.2 30.7 42.8 55.7
w/o 对比学习损失 37.1 (-0.1) 29.2 (-1.5) 40.1 (-2.7) 54.4 (-1.3)
w/o 实体关系融合 36.3 (-0.9) 27.2 (-3.5) 39.8 (-3.0) 54.7 (-1.0)
w/o 邻居信息聚合 35.2 (-2.0) 25.9 (-4.8) 39.0 (-3.8) 53.8 (-1.9)
Table 4  ERF-KGC模型在FB15K237数据集上的消融实验结果
模型 MRR/% Hits@1/% Hits@3/% Hits@10/%
ERF-KGC 49.5 48.2 54.0 63.2
w/o 对比学习损失 48.0 (-1.5) 47.9 (-0.3) 53.0 (-1.0) 62.0 (-1.2)
w/o 实体关系融合 47.5 (-2.0) 43.2 (-5.0) 50.2 (-3.8) 59.2 (-4.0)
w/o 邻居信息聚合 47.0 (-2.5) 42.3 (-5.9) 48.2 (-5.8) 55.6 (-7.6)
Table 5  ERF-KGC模型在WN18RR数据集上的消融实验结果
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