6 citations · 10 across the 12 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…
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
Wen Zhang, Zhen Yao, Mingyang Chen +2
Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling…
Analogical Inference Enhanced Knowledge Graph Embedding
Zhen Yao, Wen Zhang, Mingyang Chen +3
Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in kno…
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…