Document Type : Research Paper
Authors
1 Assistant Professor, Department of Information Science and Knowledge, Shahid Chamran University, Ahvaz, Iran
2 Ph.D. Student in Information Science and Knowledge, Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abstract
The purpose of the current research is to draw and analyze the intellectual structure and evolution of knowledge in the field of RDF with the method of co-occurrence analysis of words and clustering of concepts and events in this field. This is an applied research that was carried out with a scientometric approach. The statistical population of this research includes all the researches conducted in the field of RDF in the Web of Science database from 1998-2021. Also, the data collection tool in this research is note-taking and the data analysis tool is co-occurrence analysis of words and network analysis using Vosviewer, Netdrow, SPSS and Bibexecl software. The findings of the research showed that the keywords RDF, Semantic web, Ontology, linked data and SPARQL are the most frequent words and the keywords RDF* semantic web, RDF* Academic Ontology and RDF* SPARQL are the most frequent word pairs. Also, the co-occurrence analysis of words network includes six clusters named "data model scalability", "RDF representation of bibliographic entities and relations", "ontology alignment", "semantic web and linked data", "data management and publishing" and "data mining". In addition, the network density is equal to 0.068, which is not in a favorable condition. The clusters of "data model scalability", "ontology alignment", "data management and publishing" and "data mining" have not yet reached sufficient maturity and require a lot of follow-up and research in these fields. The results showed that the scientific productions of the RDF field, despite its upward publication trend, have more subject dispersion and are more oriented towards the semantic web, and the analysis of the co-occurrence network of words in this field also has a greater subject dispersion, which indicates the interest of researchers to various topics in this area.
1.Introduction
The abundance of publications in the field of Resource Description Framework (RDF) presents a challenge for researchers seeking a comprehensive understanding of the domain. RDF, a graph-based data model crucial to the Semantic Web, enables machine-readable data representation and interoperability across systems. The growing volume of RDF-related literature highlights the need for a structured analysis to identify key concepts, trends, and thematic evolution in this interdisciplinary field. Therefore, creating a scientific map of articles in the RDF field using the thesaurus method and presenting a strategic diagram will enhance awareness of published research status, illustrate topic relationships, identify influential topics, mature, emerging, and underdeveloped topics, thematic gaps, and establish sound scientific policies in the field. This study aims to map the intellectual structure and track the knowledge evolution in the RDF domain using scientometric approaches.
Research Question(s)
What has been the trend in scientific publications within the field of RDF from 1998 to 2021 in the Web of Science database?
How the frequency distribution of the most is commonly used keywords in RDF-related articles from 1900 to 2021?
What does the co-word network in the RDF domain look like during the period 1900 to 2021?
How are the co-word clusters in the RDF domain structured, and what are the thematic topics within each cluster from 1900 to 2021?
To what extent have the co-word clusters in the RDF domain matured over the period from 1900 to 2021
2.Literature Review
Previous studies have utilized co-word analysis in various domains such as digital libraries, military trauma, COVID-19, and knowledge management to reveal thematic structures and developmental trajectories. However, there is a gap in applying this approach specifically within the RDF domain. Studies by Alipour-Hafezi et al. (2017), Rezaeizadeh & KaramAli (2018), and Jin & Li (2019) demonstrate the effectiveness of scientometric techniques in visualizing knowledge structures, identifying research gaps, and tracing emergent topics. This study builds upon these methodological foundations to comprehensively explore the RDF field.
3.Methodology
This applied research employs a scientometric methodology grounded in co-word analysis. The dataset includes 1,271 scholarly articles published between 1998 and 2021 and indexed in the Web of Science database. Tools such as VOSviewer, Netdraw, SPSS, BibExcel, and UCINET were used to conduct word co-occurrence analysis, hierarchical clustering, and strategic diagramming. The analytical process involved keyword standardization, matrix generation, network visualization, and calculation of centrality and density indices for identified clusters.
4.Results
The research findings reveal that keywords such as RDF, Semantic Web, Ontology, Linked Data, and SPARQL are the most frequent, while word pairs like RDF* Semantic Web, RDF* Academic Ontology, and RDF* SPARQL are common. The co-occurrence analysis of the word network reveals six clusters named "data model scalability", "RDF representation of bibliographic entities and relations", "ontology alignment", "semantic web and linked data", "data management and publishing", and "data mining". The network density is 0.068, indicating a less favorable condition. Clusters like "data model scalability", "ontology alignment", "data management and publishing", and "data mining" are not yet mature and require further research.
5.Discussion
The findings suggest that while the RDF domain has seen an increase in publication volume, it still faces thematic fragmentation and limited interdisciplinary integration. High centrality in certain clusters indicates dominance, but low-density values suggest underdeveloped interrelations among concepts. This highlights the need for broader collaboration and diversification of research topics within RDF. The prevalence of semantic web topics reflects current research interests, while emerging areas like data scalability and ontology alignment require more attention.
6.Conclusion
This study offers a detailed intellectual mapping of the RDF field, highlighting dominant themes and emerging areas for further exploration. The low network density and dispersed thematic structure emphasize the need for increased interdisciplinary collaboration. Policymakers and researchers are encouraged to support studies in underdeveloped RDF subdomains to promote comprehensive scientific growth. The strategic insights provided by this analysis can guide future research priorities and contribute to the development of a cohesive knowledge structure within the RDF domain.
Keywords
Main Subjects
DOI: 10.35050/JIPM010.2018.069 [In Persian]
)