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Content analysis-based documentation and exploration of research articles

Content analysis-based documentation and exploration of research articles With the wealth of information available on the World Wide Web, it is difficult for anyone from a general user to the researcher to easily fulfill their information need. The main challenge is to categorize the documents systematically and also take into account more valuable data such as semantic information. The purpose of this paper is to develop a concept-based search system that leverages the external knowledge resources as the background knowledge for getting the accurate and efficient meaningful search results.Design/methodology/approachThe paper introduces the approach which is based on formal concept analysis (FCA) with the semantic information to support the document management in information retrieval (IR). To describe the semantic information of the documents, the system uses the popular knowledge resources WordNet and Wikipedia. By using FCA, the system creates the concept lattice as the concept hierarchy of the document and proposes the navigation algorithm for retrieving the hierarchy based on the user query.FindingsThe semantic information of the document is based on the two external popular knowledge resources; the authors find that it will be more efficient to deal with the semantic mismatch problems of user need.Originality/valueThe navigation algorithm proposed in this research is applied to the scientific articles of the National Science Foundation (NSF). The proposed system can enhance the integration and exploration of the scientific articles for the advancement of the Scientific and Engineering Research Community. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Data Technologies and Applications Emerald Publishing

Content analysis-based documentation and exploration of research articles

Data Technologies and Applications , Volume 56 (1): 20 – Jan 18, 2022

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References (41)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2514-9288
DOI
10.1108/dta-07-2020-0146
Publisher site
See Article on Publisher Site

Abstract

With the wealth of information available on the World Wide Web, it is difficult for anyone from a general user to the researcher to easily fulfill their information need. The main challenge is to categorize the documents systematically and also take into account more valuable data such as semantic information. The purpose of this paper is to develop a concept-based search system that leverages the external knowledge resources as the background knowledge for getting the accurate and efficient meaningful search results.Design/methodology/approachThe paper introduces the approach which is based on formal concept analysis (FCA) with the semantic information to support the document management in information retrieval (IR). To describe the semantic information of the documents, the system uses the popular knowledge resources WordNet and Wikipedia. By using FCA, the system creates the concept lattice as the concept hierarchy of the document and proposes the navigation algorithm for retrieving the hierarchy based on the user query.FindingsThe semantic information of the document is based on the two external popular knowledge resources; the authors find that it will be more efficient to deal with the semantic mismatch problems of user need.Originality/valueThe navigation algorithm proposed in this research is applied to the scientific articles of the National Science Foundation (NSF). The proposed system can enhance the integration and exploration of the scientific articles for the advancement of the Scientific and Engineering Research Community.

Journal

Data Technologies and ApplicationsEmerald Publishing

Published: Jan 18, 2022

Keywords: Natural language processing; Semantic similarity; WordNet; Wikipedia; Formal concept analysis; Information retrieval

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