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Concepts recommendation for searching scientific papers

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Abstract

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search.

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Acknowledgements

This work is supported by the Development Project of Jilin Province of China (Nos. 20170203002GX, 20160414009GH, 20170101006JC, 20160204022GX), the National Natural Science Foundation of China (No. 61472159), China Postdoctoral Science Foundation (No. 2016M601379) and MOE Research Center for Online Education Quantong Education Foundation (No. 2017YB131). Premier-Discipline Enhancement Scheme supported by Zhuhai Government and Premier Key-Discipline Enhancement Scheme supported Guangdong Government Funds.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, China

    Yang Chi, Jinchao Zhu, Lan Huag & Hao Xu

  2. School of Management, Jilin University, Changchun, China

    Lan Huag & Hao Xu

  3. Department of Computer Science and Technology, Zhuhai College of Jilin University, Guangdong, China

    Hao Xu

  4. Symbol Computation and Knowledge Engineer of Ministry of Education, Jilin University, Changchun, China

    Lan Huag & Hao Xu

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  1. Yang Chi
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  2. Jinchao Zhu
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  3. Lan Huag
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  4. Hao Xu
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Correspondence to Hao Xu.

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Chi, Y., Zhu, J., Huag, L. et al. Concepts recommendation for searching scientific papers. Cluster Comput 22 (Suppl 4), 8669–8675 (2019). https://doi.org/10.1007/s10586-018-1937-1

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  • DOI: https://doi.org/10.1007/s10586-018-1937-1

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