Concepts recommendation for searching scientific papers
- 8 Downloads
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.
KeywordsConcepts recommendation Data mining Information retrieval Scientific papers
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.
- 1.Poelmans, J., Ignatov, D.I., Viaene, S., Dedene, G., Kuznetsov, S.O.: Text mining scientific papers: a survey on FCA-based information retrieval research. Workshop Adv. Data Min. Ind. Conf. 7377, 273–287 (2012)Google Scholar
- 7.Valerică, G.S.: Analysis method of research papers published for audit domain, based on titles and keywords. Ann. Econ. Ser. 4(8), 53–60 (2015)Google Scholar
- 10.Liu, Z., Walker, J., & Chen, Y.(2007). XSeek: A semantic XML search engine using keywords. In: International Conference on Very Large Data Bases, pp. 1330–1333Google Scholar
- 11.Otsuka, S., Kitsuregawa, M.: Clustering of search engine keywords using access logs. Database Expert Syst. Appl. 4080, 842–852 (2006)Google Scholar