Exploring & exploiting high-order graph structure for sparse knowledge graph completion

• Main Authors: HE, Tao, LIU, Ming, CAO, Yixin, WANG, Zekun, ZHENG, Zihao, QIN, Bing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2025
• Subjects: graph neural networks; knowledge graph completion; reinforcement learning; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/9847; https://ink.library.smu.edu.sg/context/sis_research/article/10847/viewcontent/High_order_Graph_Structure_Sparse_KG_av.pdf
• Institution: Singapore Management University

Sparse Knowledge Graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of the widespread existence of sparse KGs in practical app...