13 citations · 15 across the 7 of their papers we have counts for
7 papers
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…
Ontology-Enhanced Knowledge Graph Completion using Large Language Models
Wenbin Guo, Xin Wang, Jiaoyan Chen +2
Large Language Models (LLMs) have been extensively adopted in Knowledge Graph Completion (KGC), showcasing significant research advancements. However, as black-box models driven by…
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…
Large Language Model Enhanced Knowledge Representation Learning: A Survey
Xin Wang, Zirui Chen, Haofen Wang +3
Knowledge Representation Learning (KRL) is crucial for enabling applications of symbolic knowledge from Knowledge Graphs (KGs) to downstream tasks by projecting knowledge facts int…
Qibo: A Large Language Model for Traditional Chinese Medicine
Heyi Zhang, Xin Wang, Zhaopeng Meng +5
Large Language Models (LLMs) has made significant progress in a number of professional fields, including medicine, law, and finance. However, in traditional Chinese medicine (TCM),…
HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
Zhao Li, Xin Wang, Jun Zhao +2
Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the…
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
Wenbin Guo, Zhao Li, Xin Wang +4
Knowledge graphs often suffer from incompleteness issues, which can be alleviated through information completion. However, current state-of-the-art deep knowledge convolutional emb…