TCKGE: Transformers with contrastive learning for knowledge graph embedding

doi:10.1007/s13735-022-00256-3

	TCKGE: Transformers with contrastive learning for knowledge graph embedding
	Zhang, Xiaowei1 ; Fang, Quan2 ; Hu, Jun 2; Qian, Shengsheng2 ; Xu, Changsheng2
发表期刊	INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
ISSN	2192-6611
	2022-11-27
页码	9
通讯作者	Fang, Quan(qfang@nlpr.ia.ac.cn)
摘要	Representation learning of knowledge graphs has emerged as a powerful technique for various downstream tasks. In recent years, numerous research efforts have been made for knowledge graphs embedding. However, previous approaches usually have difficulty dealing with complex multi-relational knowledge graphs due to their shallow network architecture. In this paper, we propose a novel framework named Transformers with Contrastive learning for Knowledge Graph Embedding (TCKGE), which aims to learn complex semantics in multi-relational knowledge graphs with deep architectures. To effectively capture the rich semantics of knowledge graphs, our framework leverages the powerful Transformers to build a deep hierarchical architecture to dynamically learn the embeddings of entities and relations. To obtain more robust knowledge embeddings with our deep architecture, we design a contrastive learning scheme to facilitate optimization by exploring the effectiveness of several different data augmentation strategies. The experimental results on two benchmark datasets show the superior of TCKGE over state-of-the-art models.
关键词	Augmentation Contrastive learning Knowledge graph Transformer
DOI	10.1007/s13735-022-00256-3
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62036012] ; National Natural Science Foundation of China[62072456] ; National Natural Science Foundation of China[62106262] ; Open Research Projects of Zhejiang Lab[2021KE0AB05]
项目资助者	National Natural Science Foundation of China ; Open Research Projects of Zhejiang Lab
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Software Engineering
WOS记录号	WOS:000889032300001
出版者	SPRINGER
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50780
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Fang, Quan
作者单位	1.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450001, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhang, Xiaowei,Fang, Quan,Hu, Jun,et al. TCKGE: Transformers with contrastive learning for knowledge graph embedding[J]. INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL,2022:9.
APA	Zhang, Xiaowei,Fang, Quan,Hu, Jun,Qian, Shengsheng,&Xu, Changsheng.(2022).TCKGE: Transformers with contrastive learning for knowledge graph embedding.INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL,9.
MLA	Zhang, Xiaowei,et al."TCKGE: Transformers with contrastive learning for knowledge graph embedding".INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL (2022):9.