3 papers
cs.CL2026
ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models
Wenbin Guo, Xin Wang, Jiaoyan Chen +3
Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond tra…
cs.AI2026
Disentangle-then-Refine: LLM-Guided Decoupling and Structure-Aware Refinement for Graph Contrastive Learning
Zhaoxing Li, Hai-Feng Zhang, Xiaoming Zhang
Conventional Graph Contrastive Learning (GCL) on Text-Attributed Graphs (TAGs) relies on blind stochastic augmentations, inadvertently entangling task-relevant signals with noise.…
cs.CL2025
KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models
Zirui Chen, Xin Wang, Zhao Li +2
Recent advances in knowledge representation learning (KRL) highlight the urgent necessity to unify symbolic knowledge graphs (KGs) with language models (LMs) for richer semantic un…