4 citations · 9 across the 12 of their papers we have counts for
13 papers
OmniPhys: Knowledge-Graph-Driven Benchmarking and Collective Optimization for Physical Commonsense in Text-to-Image Generation
Yajing Xu, Yarong Lan, Jiaoyan Chen +6
While text-to-image models exhibit remarkable visual fidelity, they frequently violate fundamental physical commonsense. Existing benchmarks often rely on coarse-grained descriptio…
Abstractive Visual Understanding of Multi-modal Structured Knowledge: A New Perspective for MLLM Evaluation
Yichi Zhang, Zhuo Chen, Lingbing Guo +4
Multi-modal large language models (MLLMs) incorporate heterogeneous modalities into LLMs, enabling a comprehensive understanding of diverse scenarios and objects. Despite the proli…
Multi-modal Knowledge Graph Generation with Semantics-enriched Prompts
Yajing Xu, Zhiqiang Liu, Jiaoyan Chen +7
Multi-modal Knowledge Graphs (MMKGs) have been widely applied across various domains for knowledge representation. However, the existing MMKGs are significantly fewer than required…
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…
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Wen Zhang, Yushan Zhu, Mingyang Chen +5
Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Repres…
Disentangled Ontology Embedding for Zero-shot Learning
Yuxia Geng, Jiaoyan Chen, Wen Zhang +6
Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). Ho…
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
Wen Zhang, Xiangnan Chen, Zhen Yao +10
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs. It implements three different series of Knowledge Graph Embedding (KGE) met…