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Scientometric Study of Semantic Web Researches
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Mohammad Hassan Azimi    , Sara Dakhesh * |
| PhD student, Shahid Chamran University of Ahvaz, Ahvaz, Iran , saradakhesh@gmail.com |
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Abstract: (3456 Views) |
Background and aim: The present study was conducted to determine the emergence of prominent subjects and to outline the intellectual structure of semantic web research.
Materials and methods: This descriptive and applied study was conducted using the scientometric method and social network analysis. The study population consisted of all semantic web documents indexed in Web of Science journals during 1990-2019. To do so, the “Semantic Web” (SW) keyword as general term was selected in the “Topic” field covered titles, abstracts, and keywords from the beginning to 2019 to collect data. Next, the data of 16,111 retrieved documents in plain text format of 500 characters per file were extracted on 9 October 2020. Ultimately, 34 files were entered into the CiteSpace software for the final analysis.
Findings: The results indicated that a major SW development occurred in the 21st century. Two separate periods (2007-2011, 2014-2016) had the highest publication statistics. Furthermore, “Michel Dumontier” was the most influential author while maintaining a high burst (8.50) and centrality (19) in the SW co-authorship network. “Karlsruhe” University in Germany held the highest burst (27.56) and performance at the same time among the 62 universities active in SW. Finally, the subject categories of computer science and interdisciplinary applications, engineering, control and automation systems and artificial intelligence were introduced as emerging subject categories on the SW, as these subjects demonstrated the highest centrality and burst at the same time.
Conclusion: Overall, the analysis of the keyword co-occurrence network of SW researches showed that the time trend of the emergence of these keywords started with the SW and XML as well as recently focused on the keywords including knowledge management, e-commerce, information retrieval, metadata, knowledge-based systems and network interoperability. |
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| Keywords: Semantic Web, Scientometrics, Keyword co-occurrence, Scientific drawing |
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Full-Text [PDF 1015 kb]
(1988 Downloads)
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Type of Study: Orginal |
Subject:
Scientometrics
Received: 2020/12/17 | Revised: 2021/06/27 | Accepted: 2021/05/15 | ePublished: 2021/09/1
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