Recognizing Semantic Patterns and Extracting Top Topics for the Thesaurus of Information Science and Epistemology by Relying on Advanced Text Processing and Content Analysis Techniques

Authors

1 Assistant Professor, Department of Knowledge and Information Science, Shahid Beheshti University, Tehran, Iran

2 Assistant Professor, Department of Knowledge and Information Science, Shahid Beheshti University, Tehran, Iran.

3 Msc Student, Department of Knowledge and information Science, Shahid Beheshti University, Tehran, Iran.

Abstract

In the age of information explosion, the field of information science and knowledge seeks to simplify and improve the thesaurus production process. This goal is realized by using text mining techniques and machine learning algorithms. The proposed approach includes automatically extracting topics from unstructured text data and identifying key concepts in the field of information science and knowledge. The main goal of this research is to improve and develop the thesaurus by focusing on text mining techniques. This approach effectively facilitates information retrieval and simplifies the thesaurus generation process. This study includes several main steps. First, abstracts of articles related to the field of information science and knowledge were collected from the Web of Science citation database in the period of 1968-2022. Data were preprocessed in Python to remove unnecessary characters and symbols. Then, TextRank algorithm was applied using Pandas and NLTK libraries to discover hidden topics in texts. This iterative process led to the identification of top topics in the subject area. Finally, by analyzing and comparing the existing manual thesaurus and examining the criteria of subject coherence and thematic coverage, the effectiveness of the proposed approach was evaluated and the top terms were selected. This method effectively used big data to extract key topics in the field of information science and knowledge. This study has extracted key topics and selected top topics using text mining techniques and TextRank algorithm. The results indicate the identification of 17 main issues in the field of information science and knowledge. These topics include important areas such as archives and information centers, artificial intelligence, bibliography, classification, collection development, controlled vocabulary, digital libraries, information organization, information retrieval and data extraction, information science and librarianship, information systems and resources, knowledge management, Libraries and community services are metadata, reference services, subject headings, and scientology. This list of top topics effectively represents key concepts in the field of information science and knowledge and can be used as a basis for developing a thesaurus and improving the information retrieval process. Using text mining methods and advanced algorithms, this research extracted and proposed key topics for the term Ras through detailed analysis of textual sources.

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Main Subjects

References

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History

  • Receive Date: 21 November 2024
  • Revise Date: 28 December 2024
  • Accept Date: 29 January 2025
  • First Publish Date: 02 February 2025

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