Isfahan University of Medical Sciences Health Information Management 1735-7853 15 5 2018 12 22 Study of Similarities of Terms in Title, Author's Keywords and Controlled Vocabulary for Determining the Appropriate Field in Scientometric Thematic Analysis Study of Similarities of Terms in Title, Author's Keywords and Controlled Vocabulary for Determining the Appropriate Field in Scientometric Thematic Analysis 220 225 11654 10.22122/him.v15i5.3560 FA Farideh Osareh Professor, Knowledge and Information Science, Department of Knowledge and Information Science, School of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0000-0001-6691-0339 Mohammad Tavakolizadeh-Ravari Associate Professor, Knowledge and Information Science, Department of Knowledge and Information Science, School of Social Sciences, Yazd University, Yazd, Iran 0000-0003-1649-671X Zahed Bigdeli Professor, Knowledge and Information Science, Department of Knowledge and Information Science, School of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran Roghayeh Ghazavi PhD Student, Knowledge and Information Science, Department of Knowledge and Information Science, School of Educational Sciences and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0000-0002-4829-2167 Journal Article 2018 09 14 Introduction: One problem in conducting scientometric thematic analysis is selecting which of the bibliographic fields containing the topics can be analyzed. This study aimed to compare subject fields of documents to determine the field or a combination of fields which are suitable for conducting a complete and proper thematic analysis in scientometrics.Methods: This was a descriptive research with content analysis approach. Scientific products in the field of functional gastrointestinal disorders were extracted from the Scopus database. The analysis was done on 13798 documents, which included title, author keywords, and index keywords. After clustering using the K-Means method, and calculating the inclusion index for created clusters, the similarity of keywords between the three fields was determined.Results: There was a high similarity between the index and the author keywords (87.71 and 85.71). The low amount of the index in the title field and the index keywords (0) also suggested that there was little similarity between the controlled vocabulary and the keywords used by the authors in the title, and that authors did not use the preferred vocabulary in the title.Conclusion: Using the words of the title field will show the results of the natural language analysis. However, if the purpose of a study is categorizing terms, the use of index keywords field will be the most appropriate. Introduction: One problem in conducting scientometric thematic analysis is selecting which of the bibliographic fields containing the topics can be analyzed. This study aimed to compare subject fields of documents to determine the field or a combination of fields which are suitable for conducting a complete and proper thematic analysis in scientometrics.Methods: This was a descriptive research with content analysis approach. Scientific products in the field of functional gastrointestinal disorders were extracted from the Scopus database. The analysis was done on 13798 documents, which included title, author keywords, and index keywords. After clustering using the K-Means method, and calculating the inclusion index for created clusters, the similarity of keywords between the three fields was determined.Results: There was a high similarity between the index and the author keywords (87.71 and 85.71). The low amount of the index in the title field and the index keywords (0) also suggested that there was little similarity between the controlled vocabulary and the keywords used by the authors in the title, and that authors did not use the preferred vocabulary in the title.Conclusion: Using the words of the title field will show the results of the natural language analysis. However, if the purpose of a study is categorizing terms, the use of index keywords field will be the most appropriate. https://him.mui.ac.ir/article_11654_459f9394f0bcd67a08a322b56db20dd3.pdf