Z
Zhiyuan Liu
Researcher at Tsinghua University
Publications - 284
Citations - 22033
Zhiyuan Liu is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Relationship extraction. The author has an hindex of 50, co-authored 163 publications receiving 14890 citations. Previous affiliations of Zhiyuan Liu include Google & Jiangsu Normal University.
Papers
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Proceedings Article
Learning entity and relation embeddings for knowledge graph completion
TL;DR: TransR is proposed to build entity and relation embeddings in separate entity space and relation spaces to build translations between projected entities and to evaluate the models on three tasks including link prediction, triple classification and relational fact extraction.
Posted Content
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou,Ganqu Cui,Shengding Hu,Zhengyan Zhang,Cheng Yang,Zhiyuan Liu,Lifeng Wang,Changcheng Li,Maosong Sun +8 more
TL;DR: A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.
Journal ArticleDOI
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou,Ganqu Cui,Shengding Hu,Zhengyan Zhang,Cheng Yang,Zhiyuan Liu,Lifeng Wang,Changcheng Li,Maosong Sun +8 more
TL;DR: In this paper, the authors propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.
Proceedings ArticleDOI
ERNIE: Enhanced Language Representation with Informative Entities
TL;DR: This paper utilizes both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE) which can take full advantage of lexical, syntactic, and knowledge information simultaneously, and is comparable with the state-of-the-art model BERT on other common NLP tasks.
Proceedings ArticleDOI
Neural Relation Extraction with Selective Attention over Instances
TL;DR: A sentence-level attention-based model for relation extraction that employs convolutional neural networks to embed the semantics of sentences and dynamically reduce the weights of those noisy instances.