3 accepted papers
Knowledge Graph Embedding (KGE) aims to map entities and relationships into a continuous vector space to facilitate reasoning and downstream tasks. Although previous KGE methods based on Euclidean, complex spaces, or hyperbolic spaces have performed well, they still struggle to effectively model Z-P
Transformer-based large language models face severe scalability challenges in long-context generation due to the computational and memory costs of full-context attention. Under practical computation and memory constraints, many inference-efficient long-context methods improve efficiency by adopting …
Knowledge graph embedding techniques have emerged as a critical approach for addressing the issue of missing relations in knowledge graphs. However, existing methods often suffer from limitations, including high intra-group similarity, loss of semantic information, and insufficient inference capabil…