Introduction: Despite the importance of knowledge management in healthcare, this sector is widely viewed within the literature, as accepting the concept last. There is not enough information on the topical content of the field of medical knowledge management. Therefore, this study, using scientometrics approaches, attempted to assist the understanding of knowledge trends, identifying core topics, and revealing the intellectual structure of knowledge in this field. Methods: This was a descriptive, scientometric research, using co-word analysis and hierarchical clustering. The research materials consisted of 868 documents in the PubMed, for the period 1980-2017, that included the term “ Knowledge Management” in the title and in the abstract fields. In addition, 200 Persian articles related to the topic, which were covered in Persian databases such as Magiran, Noormagz, the Scientific Information Database (SID) and the Persian Science Citation Index (PSCI) were included. Results: There was a positive growth in both sets of documents with a ratio of 2. 98 for English and 2. 31 for Persian articles. The keywords of English and Persian papers were clustered in 14 and 10 thematic clusters, respectively. To identify similarities between the two groups of documents, clustering results were compared visually. Only 13 common keywords were presented in the thematic clusters of Persian and English documents, and these were scattered across different clusters. Conclusion: Identifying and clustering core keywords lead to the conclusion that knowledge management in the field of medicine is mostly service-oriented.

Introduction: The field of digital library in today's world is diversified and developed and has an interdisciplinary nature. So that the study in its various aspects is always evaluated as one of the important topics in scientific fields and can be the origin of important researches. The purpose of this study is to investigate and plot the intellectual structure of knowledge in the field of digital library in the world using a co-word analysis.Methodology: This research is a type of applied scientometric studies that uses co-word analysis and network analysis. The statistical population was all researches in the field of digital library in the web of science database, which is equal to 5655 records in total. Vosviewer, Netdraw, SPSS and Bibexecl software were also used to analyze and plot the network.Findings: The results showed that the keywords digital library, information retrieval and library are the most frequent words and the most pairs of words are digital library and information retrieval. The co-word network includes six clusters called knowledge management and digital library, storage and retrieval of digital resources, interaction between digital and non-digital environments and e-learning, electronic resources and media, virtual screening and medical data analysis. The network density is .24 and the average centrality index is .663. The co-word network of the digital library field showed that the keyword digital library is the most central term and plays the main role of this network. The density of the network is .1290, which is not in a favorable condition. This case shows that the sub-domains within the clusters and inter-cluster connections, or the lack of research orientation to the technical issues of content, software and services as the three main elements of digital libraries and single domain. There is a lack of balance between theoretical and applied interdisciplinary research in this field. These findings are consistent with the research results of others. Of course, the low density of the network also indicates that the subjects of the library field have been researched more in a specialized way and less communication has been established between these fields, which is one of the weaknesses of this field. According to the strategic diagram, knowledge management clusters and digital libraries and digital screening are developed clusters and play a pivotal role. The electronic resources and media cluster, although pivotal, is underdeveloped and immature. Other subject clusters are marginal clusters and are emerging topics that have not been sufficiently researched.  In general, the results of this research showed that most of the researches in the field of digital library of the world have been done in the subjects of information retrieval, university libraries, user studies, internet and copyright. Considering that the network density of the digital library field is low, it is clear that researchers have neglected its interdisciplinary fields and the subjects of this field have little connection with each other. By reviewing the clusters obtained, it is concluded that the clusters of knowledge management, digital library and virtual screening have the central role of researches and most of the researches are carried out in this field and on the other hand, despite the fact that the clusters of storing and retrieving digital resources, human and environment interaction. Digital and electronic learning are one of the main and important topics in the field of digital library, they are emerging, marginal and underdeveloped topics, and researchers should direct most of their research in this direction so that these fields also reach a favorable state. In general, the structure of the intellectual network of the digital library field is fragmented, and in order to create a link between the structure of this network, researches should be removed from being single-domain and theoretical and applied interdisciplinary researches should be conducted.

Purpose: This study aims to analyze the intellectual structure of knowledge in information retrieval based on Co-Word Analysis. Nowadays, the field of information retrieval has primarily shifted from information science to computer science. In information science, information retrieval refers to the interaction between individuals, and the information retrieval system refers to making judgments related to the results obtained from the selection of search strategies. While in computer science, information retrieval is the extraction of relevant information that meets user needs from a large amount of unstructured information stored in a computer. Co-Word analysis is a method of content analysis that is obtained through the co-occurrence of words or concepts in texts and sources, and through which the main concepts of a scientific field or field, and through this knowledge, conceptual patterns and events, scientific structure, conceptual network, hierarchical relations of concepts, and conceptual categories of that field can be discovered, drawn, and managed. Co-word analysis is a tool for discovering hidden patterns and emerging conceptual events. Methodology: As an applied bibliometrics study, the research uses co-word analysis and clustering techniques. The research data consists of information retrieval of 13490 articles which were indexed in the Library, Information Science & Technology Abstracts (LISTA) database during 1967-2018. RavarpreMAP, bibExcel, SPSS, and VOSviewer were used for analysis. Findings: The results showed that during 1967-2018, the trend of publishing articles in the first two decades was steady. However, in the following two decades, there was a pattern of fluctuation, with occasional declines and rises. The Journal of the American Society for Information Science & Technology published 687 articles and has made the greatest contribution to research publication in this field. The publications in Information Processing & Management and Journal of the American Society for Information Science are ranked second and third, with 667 and 637 articles, respectively. The most frequently mentioned keyword in the text is "information storage and retrieval system, " which appears 3652 times. Other frequently mentioned topics include the World Wide Web, information resource management, information science, information services, search engines, websites, and the search for electronic information resources. These topics ranked second to tenth in terms of frequency and attention. Additionally, there is a strong co-occurrence between the following pairs: website-World Wide Web, Internet-World Wide Web, and information resource management-information storage and retrieval system. The use of hierarchical clustering by the Ward method resulted in the creation of 13 subject clusters, including computer network and multimedia systems,academic library and access to information,database and information retrieval,search strategy,data mining cluster and database,online library catalog,tutorials and web versions 1, 2, and 3,catalog and subject heading,Madeline,ontology and machine learning,library and information technology services,information organization and knowledge management,and artificial intelligence and medical informatics. Conclusion: The results showed that recent developments in information storage and retrieval systems, as well as users' need for quick access to up-to-date and sufficient information, have led to changes in the subject areas of information retrieval. These changes include the transformation of traditional libraries into virtual, digital, and automated libraries, the evolution of the one-dimensional web to the interactive web or Web2, and the management of electronic resources, databases, digital libraries, ontology, data mining, social networks, machine learning, natural language processing, knowledge management, and artificial intelligence. Information retrieval is now widely used in various fields such as medicine, social networking, image recovery, music, and more. The field of information retrieval is a multidisciplinary field and has extensive connections with other fields. The results of cluster analysis, illustration, and examination of high-frequency topics clearly demonstrate this relationship.

Purpose: To identify the patterns and trends of the Iranian LIS Studies and the changes made in the Intellectual structure of this field. Methodology: Using the keyword co-occurrence method and the knowledge Domain Visualization approach, the intellectual structure of LIS has been studied. The research community is all research papers and articles of conferences that have been published by Iranian researchers in the period from 1970 to 2016 in the LIS journals and indexed on the WOS. Citespace software was used to map and analyze the network of Iranian studies. Findings: Cluster analysis led to the identification of 10 clusters. Overall, it was found that the main focal points of the research are two general titles of "science studies" and "information studies", where the share of each of these two research centers from all identified clusters was 4, 5 Cluster. The largest cluster, according to the number of nodes, was "User Studies and Systems" and the oldest cluster based on the average year of formation, "scientific collaboration. " The results of the analysis of the keywords burstness, "Internet", "World Wide Web", "User studies" and "Search engines" respectively, have attracted the most attention of the researchers of information science and science and are among the hot topics in the time series investigated. Conclusion: The network of the key words of Iran LIS studies is immature; so that from the total of 257 nodes present in the analysis process, 71 nodes were located between 2000 and 2009. One of the important points in reflecting on the analysis of the central nodes of the keyword network is related to the low centrality scores of the word “ library” in spite of having high occurrence frequency; which may be interpreted as the reduction of pivotal role of this concept in the LIS literature.

Purpose: The purpose of this study was to investigate the intellectual structure of global research in Library and Information Science based on the analysis of the views of Iranian experts. Methodology: In the present study, the semi-structured interview method was used to obtain the views of the Iranian specialized experts, and in order to evaluate the results of the interviews, a qualitative content analysis method was used with deductive approach. Considering the qualitative nature of the research, the use of purposeful sampling and snowball method ultimately led to the identification of 10 experts. Findings: The qualitative analysis of the results of the interview with the experts of Library and Information Science led to the identification of 8 main categories and 37 sub categories. The findings showed that the methodological and interdisciplinary nature of research in scientometrics has led to its superiority over the other subsectors of Library and Information Science in terms of number of documents and number of citations. Also, the results of the content analysis of the interviews indicated that the recent trends in Library and Information Science can be summarized as a general category of "information studies". Conclusion: The interdisciplinary nature of the study areas has a significant role in the amount of scientific publications and citations received by their researchers. The results showed that countries' awareness of the close relationship between scientific growth and economic development, comprehensive investment in promoting scientific status, the allocation of significant GDP contribution to research topics, and the comprehensive support of research projects in the form of grants Research is one of the most important factors influencing the intellectual structure of the field. According to the results of this study, the weakness of the theoretical bases and the native nature of study areas can affect their reliance on scientific papers; while areas with more powerful theoretical foundations are more likely to come into the book because of the retrospective nature of the book.

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. IntroductionThe 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 20212. Literature ReviewPrevious 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. MethodologyThis 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. ResultsThe 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. DiscussionThe 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. ConclusionThis 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.

Background: Co-word analysis is one of the content analysis methods used in scientometric studies and mapping the scientific structure of various fields. The purpose of the present research is to map the structure of distance education using the co-word analysis. Methods: The research method is content analysis using co-word analysis. The research population are 31607 documents indexed in the field of distance education domain in the Web of Science database from 1985 to 2016. For data analysis, the "UCInet" and "BibExcel" softwares have been used. In this research, the symmetric matrix as well as the cluster analysis and the strategic diagram was used for analyzing the data. Results: The findings showed that the concepts of electronic learning and blended learning had the highest frequency in distance education research. The pairs of "e-learning-blended learning", "e-learning-education and training", and "higher education-e-learning" with 446, 328, and 302 word co-occurrences respectively, and took the first to third places in the field of distance education studies. Findings on hierarchical clustering led to the formation of 13 clusters in this field. Conclusion: The results of this study showed that based on the co-word analysis, the structure of the distance education is composed of thirteen clusters as follows: “ The process of designing e-learning environments” , “ E-literacy” , “ The role of information and communication technologies in the process of teaching and learning” , “ Strengthening the process of virtual teaching and learning” , “ Educational scenario” , “ educational planning” and “ Individual learning style” and “ Learning and teaching quality” , “ Human interaction in the virtual environment” , “ educational feedback” , “ Educational system” , and "Miscellaneous". Newly emerged fields of distance education include “ human interaction in the virtual environment” , “ educational feedback” , and “ learning system” .

This research is an applied research in which the thematic and methodological trends of Iran library and information science research have been studied through a scientometric approach. The research community is comprised of all research papers and proceedings that have been published by Iranian researchers in the period from 1970 to 2016 in LIS journals indexed in The WOS. Citespace is used to analyze and visualize the Iranian Co-citation network. The cluster analysis of LIS Co-citation network of Iran in the period from 1970 to 2016 led to the identification of five clusters or specialties. The title of the largest and smallest specialties in terms of the entity frequency of each cluster was awarded with 17 and 7 abundances, respectively, to two “ Web Impact Factor” and “ Information Literacy” specialties. According to the results, metric studies have played a significant role in shaping the intellectual structure of library and information science of Iran in the period studied. The results of the analysis of landmark and pivot documents in the cocitation network of Iranian studies indicated that the “ Web Impact Factor” and “ Information Behavior” were the most significant and most Pivotal specialties among other recognized specialties. The results of the content analysis of the five detected clusters in terms of their methodological structure during the period from 1970 to 2016 showed that Iranian library and information science have been influenced a great deal by the methods of metric studies.

Using a co-word analysis, this study aims to map the intellectual structure of Iran Knowledge and Information Science (KIS) in Islamic World Science Citation Database (ISC) during two 5-year periods from 2006 to 2015. This is an applied scientometric research which uses co-word analysis. The research population consists of 2467 articles which have been published in the KIS journals and indexed in the ISC. SPSS, BibExcel and UCInet are utilized for preparing and analyzing data and also for visualizing maps. Findings showed that in the first time period (2006-2010), 7 clusters, and in the second time period (2011-2016), 13 clusters were formed which in both time periods, “ scientometric cluster” is the most important one. Despite the relative overlap between clusters of the two periods, the topics in the second period were of a higher number and breadth. In both periods of study, “ university” is the most frequent keyword with 82 and 149 occurrences, respectively. “ Journals” is the next frequent keyword in the first period, and “ academic libraries” in the second period, with the occurrences of 54 and 108, respectively. The more emphasis on the IT related issues, such as digital libraries, in the second period is one of the main differences between the two time periods.