VEM 2 L: An easy but effective framework for fusing text and structure knowledge on sparse knowledge graph completion

• Main Authors: HE, Tao, LIU, Ming, CAO, Yixin, QU, Meng, ZHENG, Zihao, QIN, Bing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Centralized optimization; data-driven optimization; distributed optimization; evolutionary computation; privacy protection; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8664
• Institution: Singapore Management University

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 VEM2L, a joint learning framework that incorporates structu...