most citedKnowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.AIShow all

5 papers · 1 filter

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.AI202431 cited

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…

cs.AI20231 cited

A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

Long Jin, Zhen Yao, Mingyang Chen +2

Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with speci…

cs.AI20211 cited

Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules

Wen Zhang, Shumin Deng, Mingyang Chen +5

Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for t…

cs.AI2021

Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training

Ganqiang Ye, Wen Zhang, Zhen Bi +3

Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose…