activity
20202026
most citedKnowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

31 citations · 62 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CL2026

CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph

Chengtao Gan, Xiaoke Guo, Yushan Zhu +5

The continuous evolution of large language models drives escalating demands on data scale and quality, and as different training stages impose increasingly tailored data requiremen…

cs.CL2026

CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning

Chengtao Gan, Zhiqiang Liu, Long Jin +3

Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data increasingly important. Unifi…

cs.CL2025

Self-Correction Distillation for Structured Data Question Answering

Yushan Zhu, Wen Zhang, Long Jin +8

Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have…

cs.AI2025

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…

cs.LG2024

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…

cs.AI2024

Croppable Knowledge Graph Embedding

Yushan Zhu, Wen Zhang, Zhiqiang Liu +3

Knowledge Graph Embedding (KGE) is a common approach for Knowledge Graphs (KGs) in AI tasks. Embedding dimensions depend on application scenarios. Requiring a new dimension means t…