most citedKnowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective

13 citations · 24 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI20244 cited

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…

cs.IR2024

InBox: Recommendation with Knowledge Graph using Interest Box Embedding

Zezhong Xu, Yincen Qu, Wen Zhang +2

Knowledge graphs (KGs) have become vitally important in modern recommender systems, effectively improving performance and interpretability. Fundamentally, recommender systems aim t…

cs.LG2024

Prompt-fused framework for Inductive Logical Query Answering

Zezhong Xu, Peng Ye, Lei Liang +2

Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning. The primary obstacle in this task stems from the inherent incompleteness of…

cs.AI20226 cited

Neural-Symbolic Entangled Framework for Complex Query Answering

Zezhong Xu, Wen Zhang, Peng Ye +2

Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query…

cs.LO20221 cited

Ruleformer: Context-aware Differentiable Rule Mining over Knowledge Graph

Zezhong Xu, Peng Ye, Hui Chen +3

Rule mining is an effective approach for reasoning over knowledge graph (KG). Existing works mainly concentrate on mining rules. However, there might be several rules that could be…

cs.LG2022

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…