Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks

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成果类型：

期刊论文

作者：

Li, Zhifei;Liu, Hai*;Zhang, Zhaoli;Liu, Tingting;Xiong, Neal N.

通讯作者：

Liu, Hai

作者机构：

[Zhang, Zhaoli; Li, Zhifei; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.

[Liu, Tingting] Hubei Univ, Sch Educ, Wuhan 430062, Peoples R China.

[Xiong, Neal N.] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.

[Xiong, Neal N.] Northeastern State Univ, Dept Math & Comp Sci, Tahlequah, OK 74464 USA.

通讯机构：

[Liu, Hai] C

Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.

语种：

英文

关键词：

Semantics;Task analysis;Aggregates;Graph neural networks;Computer architecture;Learning systems;Fuses;Graph heterogeneity;graph neural networks (GNNs);knowledge graph (KG) embedding;KGs;link prediction

期刊：

IEEE Transactions on Neural Networks and Learning Systems

ISSN：

2162-237X

年：

2022

卷：

期：

页码：

3961-3973

DOI：

10.1109/TNNLS.2021.3055147

基金类别：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [6201101288, 62077020, 62005092, 61875068, 61505064]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [2020YBZZ006, CCNU20ZT017, CCNU2020ZN008]

机构署名：

本校为第一且通讯机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

Knowledge graph (KG) embedding aims to study the embedding representation to retain the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph representation technique, have shown impressive performance in learning graph embedding. However, KGs have an intrinsic property of heterogeneity, which contains various types of entities and relations. How to address complex graph data and aggregate multiple types of semantic information simultaneously is a critical issue. In this article, a novel heterogeneous GNNs framework based on attention mechanism is proposed. Specificall...

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Learning Knowledge Graph Embedding With Heterogeneous Relation Attention Networks

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