11 citations · 42 across the 17 of their papers we have counts for
5 papers · 1 filter
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
Xiangnan Chen, Wen Zhang, Zhen Yao +2
Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting the positive and negative…
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…
Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding
Yichi Zhang, Mingyang Chen, Wen Zhang
Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, exi…
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…
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
Mingyang Chen, Wen Zhang, Yuxia Geng +3
Knowledge graphs (KGs) have become valuable knowledge resources in various applications, and knowledge graph embedding (KGE) methods have garnered increasing attention in recent ye…