基于交互式连接图注意力网络的知识图谱补全方法

doi:10.12141/j.issn.1000-565X.220384

华南理工大学学报(自然科学版) ›› 2022, Vol. 50 ›› Issue (12): 13-19.doi: 10.12141/j.issn.1000-565X.220384

所属专题： 2022年计算机科学与技术

基于交互式连接图注意力网络的知识图谱补全方法

陆以勤¹ 潘周双¹ 张洋² 覃健诚¹ 黄方¹

^1.华南理工大学电子与信息学院，广东广州 510640
^2.华南理工大学计算机科学与工程学院，广东广州 510006

收稿日期:2022-06-17 出版日期:2022-12-25 发布日期:2022-08-12
通信作者: 陆以勤（1968-），男，教授，博士生导师，主要从事知识图谱、深度学习、网络安全研究。 E-mail:eeyqlu@scut.edu.cn
作者简介:陆以勤（1968-），男，教授，博士生导师，主要从事知识图谱、深度学习、网络安全研究。
基金资助:
广东省重点领域研发计划项目(2019B010137001)

Knowledge Graph Completion Method Based on Interactively Connected Graph Attention Network

LU Yiqin¹PAN Zhoushuang¹ ZHANG Yang² QIN Jiancheng¹ HUANG Fang¹

^1.School of Electronic and Information Engineering，South China University of Technology，Guangzhou 510640，Guangdong，China
^2.School of Computer Science and Engineering，South China University of Technology，Guangzhou 510006，Guangdong，China

Received:2022-06-17 Online:2022-12-25 Published:2022-08-12
Contact: 陆以勤（1968-），男，教授，博士生导师，主要从事知识图谱、深度学习、网络安全研究。 E-mail:eeyqlu@scut.edu.cn
About author:陆以勤（1968-），男，教授，博士生导师，主要从事知识图谱、深度学习、网络安全研究。
Supported by:
the Key-Area R&D Project of Guangdong Province(2019010137001)

摘要/Abstract

摘要：

知识图谱为许多智能信息服务应用提供了底层支持，包括智能搜索、公共安全、金融、医疗等领域，但现有的知识图谱通常是不完整的，知识图谱补全已经成为亟需解决的问题。现有的知识图谱补全方法忽略了邻居节点以及关系所富含的重要信息，往往只是简单地将邻居节点和关系拼接起来，忽略了不同关系和邻居节点对于节点有着不同的重要性。为此，文中提出了一种基于交互式连接图注意力网络的知识图谱补全方法（ICGAT）。该方法首先通过寻找两跳邻居节点，挖掘出潜在可能的关系，扩充每个节点的三元组；然后将每个三元组中的关系与节点的特征融合，并且采用节点与邻居节点交互式连接的方法，用4个空间向量来表示交互式连接的关系；最后将交互式连接的向量输入图注意力网络，得到关系和邻居节点对该节点的权重，以此说明其重要性。为了有效地表示一对多、多对多等复杂关系的三元组，该方法使用RotatE模型作为预训练模型。在链接预测任务中的实验结果表明，ICGAT方法在WN18RR和FB15k-237数据集中的平均排名（MR）和排名前10命中率（HR@10）均有一定的提升，说明ICGAT能够提高链接预测任务的准确性。

关键词: 知识图谱, 图注意力网络, 图神经网络, 交互式连接

Abstract:

Knowledge graph provides underlying support for many intelligent information service applications, including intelligent search, public safety, finance, medical care and other fields. However, the existing knowledge graph is usually incomplete, and knowledge graph completion has become an urgent problem to be solved. The existing knowledge graph completion method models ignore the important information rich in neighbor nodes and relationships, and often simply splice neighbor nodes and relationships together, ignoring the different importance of different relationships and neighbor nodes to nodes. To solve this problem, this paper proposed a knowledge graph completion method (ICGAT) based on the interactive connection graph attention network. The method firstly finds out the potential relationship by finding two-hop neighbor nodes, and expands the triples of each node. Then it fuses the relationship in each triplet with the features of the node, and adopts the method of interactive connection between nodes and neighbor nodes, using 4 space vectors to represent the interactively connected relationship. Finally, the vector of interactive connection was input into the graph attention network to obtain the weight of relations and neighbor nodes to the node, so as to illustrate its importance. In order to effectively represent triples of complex relationships such as one-to-many, many-to-many, etc., this method used the RotatE model as a pre-training model. The experimental results in the link prediction task show that the performance of the mean rank and HR@10indicators of the ICGAT method in the WN18RR and FB15k-237 datasets have been improved to a certain extent, indicating that ICGAT can improve the accuracy of the link prediction task.

Key words: knowledge graph, graph attention network, graph neural network, interactive connectivity

中图分类号:

TP391.1

陆以勤, 潘周双, 张洋, 等. 基于交互式连接图注意力网络的知识图谱补全方法[J]. 华南理工大学学报(自然科学版), 2022, 50(12): 13-19.

LU Yiqin, PAN Zhoushuang, ZHANG Yang, et al. Knowledge Graph Completion Method Based on Interactively Connected Graph Attention Network[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 13-19.

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数据集	实体	关系	训练集	验证集	测试集
FB15k-237	14 541	237	272 115	17 535	20 466
WN18RR	40 943	11	86 835	3 034	3 134

模型	FB15k-237			WN18RR
模型	HR@10/%	MR	MRR	HR@10/%	MR	MRR
TransE	44.1	323	27.9	53.2RRR	2 300	24.3
RotatE	53.3	177	33.8	57.1	3 340	47.6
ConvE	49.7	245	31.2	53.1	4 464	45.6
ConvKB	47.1	216	28.9	55.8	1 295	26.5
InteractE	53.5	172	35.4	52.8	5 202	46.3
ReInceptionE	52.8	173	34.9	58.2	1 894	48.3
JointE	54.3	177	35.6	53.7	4 655	47.1
SHKE	54.3	186	35.2	54.0	3 205	45.2
KBGAT	62.6	210	51.8	58.1	1 940	44.0
ICGAT	63.1	157	49.9	58.3	1 786	43.8

模型	FB15k-237数据集			WN18RR数据集
模型	HR@10/%	MR	MRR	HR@10/%	MR	MRR
ICGAT-0	62.4	166	49.5	57.7	1 985	42.6
ICGAT	63.1	157	49.9	58.3	1 786	43.8

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基于交互式连接图注意力网络的知识图谱补全方法

Knowledge Graph Completion Method Based on Interactively Connected Graph Attention Network

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