ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (2): 247-261.doi: 10.7544/issn1000-1239.2016.20160020

所属专题: 2016数据融合与知识融合专题

• 软件技术 • 上一篇    下一篇

知识表示学习研究进展

刘知远,孙茂松,林衍凯,谢若冰   

  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

摘要: 人们构建的知识库通常被表示为网络形式,节点代表实体,连边代表实体间的关系.在网络表示形式下,人们需要设计专门的图算法存储和利用知识库,存在费时费力的缺点,并受到数据稀疏问题的困扰.最近,以深度学习为代表的表示学习技术受到广泛关注.表示学习旨在将研究对象的语义信息表示为稠密低维实值向量,知识表示学习则面向知识库中的实体和关系进行表示学习.该技术可以在低维空间中高效计算实体和关系的语义联系,有效解决数据稀疏问题,使知识获取、融合和推理的性能得到显著提升.介绍知识表示学习的最新进展,总结该技术面临的主要挑战和可能解决方案,并展望该技术的未来发展方向与前景.

关键词: 知识表示, 表示学习, 知识图谱, 深度学习, 分布式表示

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.

Key words: knowledge representation, representation learning, knowledge graph, deep learning, distributed representation

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