Contrastive Multi-Modal Knowledge Graph Representation Learning

doi:10.1109/TKDE.2022.3220625

	Contrastive Multi-Modal Knowledge Graph Representation Learning
	Fang, Quan1 ; Zhang, Xiaowei2 ; Hu, Jun 1; Wu, Xian 3; Xu, Changsheng1,4
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
	2023-09-01
卷号	35 期号:9 页码:8983-8996
通讯作者	Xu, Changsheng(csxu@nlpr.ia.ac.cn)
摘要	Representation learning of knowledge graphs (KGs) aims to embed both entities and relations as vectors in a continuous low-dimensional space, which has facilitated various applications such as link prediction and entity retrieval. Most existing KG embedding methods focus on modeling the structured fact triples independently and ignore the multi-type relations among triples as well as the variety of data types (e.g., texts and images) associated with entities in KGs, and thus fail to capture the complex and multi-modal information that is inherently inside the entity-relation triples. In this paper, we propose a novel approach for knowledge graph embedding named Contrastive Multi-modal Graph Neural Network (CMGNN), which can encapsulate comprehensive features from multi-modal content descriptions of entities and high-order connectivity structures. Specifically, CMGNN first learns entity embeddings from multi-modal content and then contrasts encodings from multi-relational local neighbors and high-order connectivities to obtain latent representations of entities and relations simultaneously. Experimental results demonstrate that CMGNN can effectively model the multi-modalities and multi-type structures in KGs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the tasks of link prediction and entity classification.
关键词	Knowledge graph multimedia graph neural network contrastive learning
DOI	10.1109/TKDE.2022.3220625
关键词[WOS]	NETWORK
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62072456] ; National Natural Science Foundation of China[62036012] ; 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 ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:001045704800021
出版者	IEEE COMPUTER SOC
引用统计	被引频次：1[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/173211/53970
专题	多模态人工智能系统全国重点实验室
通讯作者	Xu, Changsheng
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Zhengzhou Univ, Zhengzhou 450001, Peoples R China 3.Tencent Med AI Lab, Beijing 100080, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位	模式识别国家重点实验室
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	Fang, Quan,Zhang, Xiaowei,Hu, Jun,et al. Contrastive Multi-Modal Knowledge Graph Representation Learning[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(9):8983-8996.
APA	Fang, Quan,Zhang, Xiaowei,Hu, Jun,Wu, Xian,&Xu, Changsheng.(2023).Contrastive Multi-Modal Knowledge Graph Representation Learning.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(9),8983-8996.
MLA	Fang, Quan,et al."Contrastive Multi-Modal Knowledge Graph Representation Learning".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.9(2023):8983-8996.