1.2k citations · 1.2k across the 14 of their papers we have counts for
15 papers
Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
Yuyuan Feng, Zhishang Xiang, Chaobin Yang +32
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit…
Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning
Xingrui Zhuo, Jiapu Wang, Manzong Huang +2
Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence available in KGs, posing a challenge to the structural semantic understanding ca…
ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs
Xinghe Cheng, Jiapu Wang, Chaobo He +2
Prerequisite relation learning is central to adaptive instruction, yet existing methods often formulate it as conventional link prediction, limiting their ability to adaptively int…
AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models
Tingzheng Jia, Kan Guo, Lanping Qian +6
Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-action (VLA) policies generate actions within…
Hybrid-Collaborative Augmentation and Contrastive Sample Adaptive-Differential Awareness for Robust Attributed Graph Clustering
Tianxiang Zhao, Youqing Wang, Jinlu Wang +4
Due to its powerful capability of self-supervised representation learning and clustering, contrastive attributed graph clustering (CAGC) has achieved great success, which mainly de…
Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning
Xingrui Zhuo, Jiapu Wang, Gongqing Wu +4
Inductive Knowledge Graph Reasoning (KGR) aims to discover facts in open-domain KGs containing unknown entities and relations, which poses a challenge for KGR models in comprehendi…
GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation
Xinghe Cheng, Zihan Zhang, Jiapu Wang +5
Learning path recommendation seeks to provide learners with a structured sequence of learning items (\eg, knowledge concepts or exercises) to optimize their learning efficiency. De…
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to Global
Jinlu Wang, Yanfeng Sun, Jiapu Wang +3
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in various graph representation learning tasks. However, most existing GNNs focus primarily on capturing loc…
HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation
Tengfei Liu, Jiapu Wang, Yongli Hu +5
Radiology report generation (RRG) models typically focus on individual exams, often overlooking the integration of historical visual or textual data, which is crucial for patient f…
Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs
Xingrui Zhuo, Jiapu Wang, Gongqing Wu +2
Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the general…
Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion
Kai Sun, Jiapu Wang, Huajie Jiang +2
Conventional Knowledge graph completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to pe…
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
Jiapu Wang, Kai Sun, Linhao Luo +5
Temporal Knowledge Graph Reasoning (TKGR) is the process of utilizing temporal information to capture complex relations within a Temporal Knowledge Graph (TKG) to infer new knowled…
IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
Jiapu Wang, Zheng Cui, Boyue Wang +4
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world.…
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Jiapu Wang, Boyue Wang, Meikang Qiu +8
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and i…
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Shirui Pan, Linhao Luo, Yufei Wang +3
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability…