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Entity set expansion in knowledge graph: a heterogeneous information network perspective

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Abstract

Entity set expansion (ESE) aims to expand an entity seed set to obtain more entities which have common properties. ESE is important for many applications such as dictionary construction and query suggestion. Traditional ESE methods relied heavily on the text and Web information of entities. Recently, some ESE methods employed knowledge graphs (KGs) to extend entities. However, they failed to effectively and efficiently utilize the rich semantics contained in a KG and ignored the text information of entities in Wikipedia. In this paper, we model a KG as a heterogeneous information network (HIN) containing multiple types of objects and relations. Fine-grained multi-type meta paths are proposed to capture the hidden relation among seed entities in a KG and thus to retrieve candidate entities. Then we rank the entities according to the meta path based structural similarity. Furthermore, to utilize the text description of entities in Wikipedia, we propose an extended model CoMeSE++ which combines both structural information revealed by a KG and text information in Wikipedia for ESE. Extensive experiments on real-world datasets demonstrate that our model achieves better performance by combining structural and textual information of entities.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61806020, 61772082, 61972047, 61702296), the National Key Research and Development Program of China (2017YFB0803304), the Beijing Municipal Natural Science Foundation (4182043), the CCF-Tencent Open Fund, and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Chuan Shi, Jiayu Ding, Xiaohuan Cao, Linmei Hu & Bin Wu

  2. Agency for Science, Technology and Research, Institute for Infocomm Research, Singapore, 138632, Singapore

    Xiaoli Li

Authors
  1. Chuan Shi
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  2. Jiayu Ding
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  3. Xiaohuan Cao
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  4. Linmei Hu
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  5. Bin Wu
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  6. Xiaoli Li
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Corresponding author

Correspondence to Linmei Hu.

Additional information

Chuan Shi received the BS degree from the Jilin University, China in 2001, the MS degree from the Wuhan University, China in 2004, and PhD degree from the ICT of Chinese Academic of Sciences, China in 2007. He joined the Beijing University of Posts and Telecommunications as a lecturer in 2007, and is a professor and deputy director of Beijing Key Lab of Intelligent Telecommunications Software and Multimedia at present. His research interests are in data mining, machine learning, and evolutionary computing. He has published more than 40 papers in refereed journals and conferences.

Jiayu Ding received the BS degree from the Beijing University of Posts and Telecommunications (BUPT), China in 2017. He is currently working toward the MS degree in the School of Computer Science at BUPT, China. His research interests are in data mining and nature language process.

Xiaohuan Cao received the BS degree from the Beijing University of Posts and Telecommunications (BUPT), China in 2015, the MS degree in the School of Computer Science at BUPT, China in 2018. Her research interests are in data mining and machine learning, especially heterogeneous information network studies.

Linmei Hu is an assistant professor in School of Computer Sciences, Beijing University of Posts and Communications, China. She received her PhD degree from Tsinghua University, China in 2018. Her research interests focus on natural language processing and data mining. She was awarded Beijing Excellent PhD Student in 2018.

Bin Wu received the BS degree from the Beijing University of Posts and Telecommunications, China in 1991, and the MS and PhD degrees from the ICT of Chinese Academic of Sciences, China in 1998 and 2002, respectively. He joined the Beijing University of Posts and Telecommunications as a lecturer in 2002, and is a professor at present. His research interests include data mining, complex network, and cloud computing. He has published more than 100 papers in refereed journals and conferences. He is a member of the IEEE.

Xiaoli Li is currently a department head at the Institute for Infocomm Research, A*STAR, Singapore. He also holds adjunct professor positions at the National University of Singapore and Nanyang Technological University. His research interests include data mining, machine learning, AI, and bioinformatics. He has served as a (senior) PC member/workshop chair/session chair in leading data mining related conferences (including KDD, ICDM, SDM, PKDD/ECML, WWW, IJCAI, AAAI, ACL and CIKM). Xiaoli has published more than 160 high quality papers and won best paper/benchmark competition awards.

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Shi, C., Ding, J., Cao, X. et al. Entity set expansion in knowledge graph: a heterogeneous information network perspective. Front. Comput. Sci. 15, 151307 (2021). https://doi.org/10.1007/s11704-020-9240-8

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