31 citations · 62 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
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…
TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
Wen Zhang, Long Jin, Yushan Zhu +6
Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs…
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Zhuo Chen, Yichi Zhang, Yin Fang +12
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for…
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…
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
Mingyang Chen, Wen Zhang, Zhen Yao +4
We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional kno…
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…
Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
Mingyang Chen, Wen Zhang, Yushan Zhu +4
Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and…
Knowledge Perceived Multi-modal Pretraining in E-commerce
Yushan Zhu, Huaixiao Tou, Wen Zhang +4
In this paper, we address multi-modal pretraining of product data in the field of E-commerce. Current multi-modal pretraining methods proposed for image and text modalities lack ro…
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
Yushan Zhu, Wen Zhang, Mingyang Chen +4
Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and training KGEs with higher dimension are usually preferred since they have better reasoning capability. Howe…