105 citations · 150 across the 17 of their papers we have counts for
9 papers
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Xuan Lin, Lichang Dai, Yafang Zhou +9
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially…
Towards Better Entity Linking with Multi-View Enhanced Distillation
Yi Liu, Yuan Tian, Jianxun Lian +7
Dense retrieval is widely used for entity linking to retrieve entities from large-scale knowledge bases. Mainstream techniques are based on a dual-encoder framework, which encodes…
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…
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…
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…