Contrastive Multi-Modal Knowledge Graph Representation Learning
文献类型：期刊论文
| 作者 | Fang, Quan2 ; Zhang, Xiaowei1; Hu, Jun2; Wu, Xian4; Xu, Changsheng2,3
|
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 35期号:9页码:8983-8996 |
| ISSN号 | 1041-4347 |
| 关键词 | Knowledge graph multimedia graph neural network contrastive learning |
| DOI | 10.1109/TKDE.2022.3220625 |
| 通讯作者 | Xu, Changsheng(csxu@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | NETWORK |
| 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE COMPUTER SOC |
| WOS记录号 | WOS:001045704800021 |
| 资助机构 | National Natural Science Foundation of China ; Open Research Projects of Zhejiang Lab |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53969]  |
| 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.Zhengzhou Univ, Zhengzhou 450001, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Tencent Med AI Lab, Beijing 100080, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。