4 citations · 9 across the 12 of their papers we have counts for
7 papers · 1 filter
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking
Yichi Zhang, Zhuo Chen, Lingbing Guo +8
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud h…
Is Large Language Model Good at Triple Set Prediction? An Empirical Study
Yuan Yuan, Yajing Xu, Wen Zhang
The core of the Knowledge Graph Completion (KGC) task is to predict and complete the missing relations or nodes in a KG. Common KGC tasks are mostly about inferring unknown element…
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network
Yushan Zhu, Wen Zhang, Yajing Xu +3
Graph Neural Network (GNN), with the main idea of encoding graph structure information of graphs by propagation and aggregation, has developed rapidly. It achieved excellent perfor…
Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion
Wen Zhang, Yajing Xu, Peng Ye +5
Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They ar…
NativE: Multi-modal Knowledge Graph Completion in the Wild
Yichi Zhang, Zhuo Chen, Lingbing Guo +5
Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively modeling…
Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
Yichi Zhang, Zhuo Chen, Lingbing Guo +5
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks withi…
Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
Yichi Zhang, Zhuo Chen, Lingbing Guo +5
Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples…