ISSN 1000-1239 CN 11-1777/TP

• 软件技术 •

### 知识表示学习研究进展

1. (清华大学计算机科学与技术系 北京 100084) (智能技术与系统国家重点实验室(清华大学) 北京 100084) (清华信息科学与技术国家实验室(筹) 北京 100084) (liuzy@tsinghua.edu.cn)
• 出版日期: 2016-02-01
• 基金资助:
国家“九七三”重点基础研究发展计划基金项目(2014CB340501)；国家自然科学基金项目(61572273，61532010)；清华大学自主科研计划基金项目(2015THZ)

### Knowledge Representation Learning: A Review

Liu Zhiyuan, Sun Maosong, Lin Yankai, Xie Ruobing

1. (Department of Computer Science and Technology, Tsinghua University, Beijing 100084) (State Key Laboratory of Intelligent Technology and Systems (Tsinghua University), Beijing 100084) (Tsinghua National Laboratory for Information Science and Technology, Beijing 100084)
• Online: 2016-02-01

Abstract: Knowledge bases are usually represented as networks with entities as nodes and relations as edges. With network representation of knowledge bases, specific algorithms have to be designed to store and utilize knowledge bases, which are usually time consuming and suffer from data sparsity issue. Recently, representation learning, delegated by deep learning, has attracted many attentions in natural language processing, computer vision and speech analysis. Representation learning aims to project the interested objects into a dense, real-valued and low-dimensional semantic space, whereas knowledge representation learning focuses on representation learning of entities and relations in knowledge bases. Representation learning can efficiently measure semantic correlations of entities and relations, alleviate sparsity issues, and significantly improve the performance of knowledge acquisition, fusion and inference. In this paper, we will introduce the recent advances of representation learning, summarize the key challenges and possible solutions, and further give a future outlook on the research and application directions.