A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal

Author:

Liang Ke1ORCID,Meng Lingyuan1ORCID,Liu Meng1ORCID,Liu Yue1ORCID,Tu Wenxuan1ORCID,Wang Siwei1ORCID,Zhou Sihang2ORCID,Liu Xinwang1ORCID,Sun Fuchun3ORCID,He Kunlun4ORCID

Affiliation:

1. School of Computer, National University of Defense Technology, Changsha, China

2. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China

3. Department of Computer Science and Technology, Tsinghua University, Beijing, China

4. Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A semi-supervised framework fusing multiple information for knowledge graph entity alignment;Expert Systems with Applications;2025-01

2. Construction of Knowledge Graphs: Current State and Challenges;Information;2024-08-22

3. Local-Global History-Aware Contrastive Learning for Temporal Knowledge Graph Reasoning;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion;2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD);2024-05-08

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