VEM2L: an easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion

• Published in: Data mining and knowledge discovery Vol. 38; no. 2; pp. 343 - 371
• Main Authors: He, Tao, Liu, Ming, Cao, Yixin, Qu, Meng, Zheng, Zihao, Qin, Bing
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Distillation; Information Storage and Retrieval; Knowledge; Knowledge acquisition; Knowledge representation; Physics; Statistics for Engineering; Training; Knowledge fusion; Knowledge graph reasoning; Variational inference; Graph neural network

The task of Knowledge Graph Completion (KGC) is to infer missing links for Knowledge Graphs (KGs) by analyzing graph structures. However, with increasing sparsity in KGs, this task becomes increasingly challenging. In this paper, we propose VEM 2 L, a joint learning framework that incorporates struc...