Knowledge Retrieval and Semantic Systems

Abstract Objective: To analyze the indexed scientific productions related to the role of artificial intelligence in the evolution of information seeking behavior in the Web of Science citation database in this field.
Method: The present study is an applied study with a scientometric approach. Data were extracted using the advanced search strategy in the Web of Science database in the period from 1976 to 2025 records. The analyses were performed using Excel, R. Studio, and Bibliometrics software.
Findings: The field of “Artificial Intelligence in the Evolution of Information Seeking Behavior” has experienced rapid growth in recent years; so that more than 39 percent of the 1902 records extracted from the aforementioned database belong to the years 2024 and 2025. Harvard University (33 articles) and author Liu (9 articles) are in the top ranks of institutions and authors, respectively. Keyword cluster analysis revealed seven thematic axes, including digital health, machine learning algorithms, natural language processing, generative artificial intelligence, recommender systems, and specialized information retrieval.
Conclusion: The field of study is a dynamic interdisciplinary field. In recent years, the need to review concepts such as trust, ethics, and information justice has become more prominent. In this direction, the humanities have gradually found a more effective place in the discourse of this field; however, continuing this trend and preventing the trend towards purely technical systems requires the expansion of deeper interdisciplinary collaborations, attention to the cognitive consequences of users' interaction with artificial intelligence, and the development of ethical frameworks for intelligent information retrieval systems.

Abstract Abstract

Organizing information in digital archives, particularly historical ones such as the Islamic Revolution Document Center of Iran, faces challenges like the inefficiency of traditional information retrieval systems. This center, with over 4.5 million document pages, 31 thousand hours of oral history, and millions of news titles and articles, requires innovative approaches such as domain ontology and knowledge graphs to improve semantic access to key entities. The aim of this research is to model a domain ontology for the digital archive of this center using a hybrid approach of text analysis and reuse of existing ontologies. The research method is mixed: semi-automatic and automatic text analysis for entity extraction, model design based on ontology principles, and validation of findings using the nominal group technique. The findings include 535 classes that, after validation with criteria of 1) hierarchical logic, 2) historical/cultural accuracy, 3) alignment and equivalence, and 4) completeness and simplicity, resulted in 457 classes confirmed, 70 classes added, 53 classes relocated in the hierarchy, and 19 classes removed. This ontology provides a foundation for a knowledge graph, enhances semantic retrieval, and serves as a logical basis for artificial intelligence systems in managing Iran's historical documents. This research fills the gap in designing practical ontologies for contemporary Iranian history and is extensible to similar domains.

Abstract Today's complex and unstable environments require the design of business models appropriate to critical conditions; the business model is the main logic of the company for creating and absorbing value within a value network, and failure to design an appropriate model leads to the failure of organizations to achieve their goals. This research aims to identify the key dimensions of a business model appropriate to critical conditions using a semantic network. The present study is applied in terms of purpose and mixed in terms of method and uses quantitative and qualitative content analysis. In this study, 150 scientific articles published in Scopus, Web of Science, and John Wiley databases between 2020 and 2025 were examined based on the PRISMA guidelines and two-stage preprocessing was performed on them; conceptual interactions between business model components were identified using VOSviewer and Gephi software in the form of 37 key terms and eight key clusters. The clusters of “strategy and crisis management”, “innovation and resilience”, “financial and revenue models”, “customer and market relations”, “business model”, “entrepreneurial capabilities”, “organizational resources and structure” and “preventive strategic actions” can be defined as key dimensions of a business model appropriate to crisis conditions. Among these, the concepts of “business model”, “crisis management”, “innovation”, “resilience” and “flexibility” were identified as central and important nodes. Cluster structure analysis showed that the business model cluster has stronger connections with other clusters and the innovation cluster establishes the most bilateral links with the entrepreneurial capabilities cluster.

Abstract The purpose of this study is analysis and visualization of articles published on clinical research data management. It is an applied study conducted with a scientometric approach. The statistical population of this study includes all publicatioons conducted on research data management in the medical field indexed in the Clarivate Analytics and Scopus databases during 1990 to 2023. The data were collected through by saving the search results in Excel sheets, and the analysis was conducted using a combination of bibliometric tools, including ScientoPy, Ravar-matrix, VOSviewer, UCINET and Python software. The findings showed that the number of articles in Clarivate Analytics and Scopus has grown significantly over the period of 1990 to 2023. The United States, the United Kingdom, and Germany respectively rank first to third in production of research data management articles in the medical field. In general, the trend of publishing scientific products in the field of clinical research data management can be assessed as desirable. Identifying leading countries and researchers in the field of clinical research data management can assist policymaking and planning research related to this field.

Abstract The present study aimed to investigate the effect of knowledge sharing on the metacognition of English as a foreign language (EFL) teachers with the mediation of creativity in the era of Covid-19. The research method was applied in terms of purpose and descriptive survey in terms of method. The statistical population of the research includes English language teaching faculty members who were teaching in state and non-state universities of Iran. Out of a total of 450 questionnaires sent to the teachers' emails, 300 were returned, of which 288 questionnaires were found usable. Therefore, the sample size was considered equal to 288 based on the available sampling method. Data collection was done using three questionnaires, include, knowledge sharing, creativity, and metacognition. The results of Cronbach's test showed a desirable level of reliability for the knowledge-sharing questionnaire (0.793), the creativity questionnaire (0.921), and the metacognition questionnaire (0.752). In order to analyze the data, descriptive statistics, multiple regression, and path analysis were used with SPSS 23 and Amos 21 software. The findings of the research showed that the variables of participation in writing, organizational communication, personal relationships, and group activity were able to explain the changes related to creativity. Among these variables, only participation in writing could explain the changes related to metacognition. In addition, the creativity variable was able to explain changes related to metacognition. In general, the results indicated that teachers who have higher degrees of knowledge-sharing are more creative in teaching and this may help to develop metacognition.

Abstract Objective: This study was conducted with the aim of identifying the effective components on improving the quality of the student information service system at the information service desks of public universities in the country.
Methodology: The present study is philosophically based on the pragmatism model, which selected the components with a partial approach and finally selected the most effective ones by comparing them. In terms of purpose, it is an applied-developmental research. In order to achieve the goal, a mixed exploratory research design (qualitative-quantitative) was used. The population of participants in the qualitative section were information science and knowledge specialists and experienced managers of public universities in Tehran, who were selected using theoretical and purposeful sampling methods, and theoretical saturation was achieved by conducting 10 interviews. The statistical population of the quantitative section includes students of public universities in Tehran, whose sample size was estimated to be 384 people using the Cochran formula. The required sample size was provided by simple random sampling. Semi-structured interviews and a researcher-made questionnaire were used to collect data. The service desk pathology model constructs were identified using qualitative content analysis in Maxqda 20 software, and the final model was validated using partial least squares in Smart PLS 3 software.

Abstract In the digital economy, data serve as strategic assets that play a vital role in creating economic value. This study aims to systematically analyze and classify the existing literature to identify frameworks and models of data valuation in information services and to explain their relationship with the cost of information services across different industries using the PRISMA guideline. The research addresses three key questions: (1) What are the fundamental criteria for data valuation? (2) What are the quantitative methods for measuring data value in information services? (3) How is the relationship between data value and the costs of information services described in the scientific literature, and what factors influence this relationship?
A systematic literature review (PRISMA) of 138 studies (2008-2025) identified 24 articles for analysis. Findings reveal three data valuation dimensions: financial (production/transaction costs), content-based (quality/entropy), and functional (operational utility). More than 75% of the reviewed studies confirm a positive relationship between data value and service cost. Despite the diversity of measurement methods—particularly in entropy-based calculations—data quality has a significant impact on its value. Among the reviewed models, hybrid and AI-driven approaches show superior performance in multidimensional analysis of data value. The proposed framework, encompassing these three dimensions, provides a foundation for developing data valuation models in information services. The heterogeneity of existing approaches highlights the need for nonlinear models and future empirical studies. The identified patterns form a basis for designing data valuation models applicable to specific industries as well as broader economic contexts.

Abstract Introduction: . The aim of the present research is to identify and rank the factors influencing knowledge sharing in the public library institutions of the country and to provide a model for implementing knowledge sharing in that organization.

Methodology: In this research, a mixed-method approach has been used, employing various tools for data collection. These tools include library studies, semi-structured interviews, and questionnaires. The population of this research includes managers, administrative staff, and county heads in the public library institutions of the country. The research sample consists of 28 experts selected, and sampling was done using cluster sampling method.

Findings: The results of the comprehensive analysis led to the identification of 92 codes, 13 indicators, and 4 main dimensions of technological, cultural, organizational, and individual aspects in the implementation of knowledge sharing.

Conclusion: The results showed that the technological dimension has the most significant impact, while the cultural dimension has the least impact on the implementation of knowledge sharing in the public library institutions of the country. Ultimately, the model of implementing knowledge sharing at 6 levels, with indicators of functional processes, internal processes, strategic processes, and organizational belonging perception being the most influential, and indicators of information acquisition processes and knowledge transfer processes being the most affected.

Abstract The Holy Defense is a lasting epic in Iran’s history, shaped by faith, national unity, and the leadership of Imam Khomeini (RA). This study aims to map the scientific network of articles related to the Holy Defense using a scientometric approach. Through co-word and network analysis, it explores conceptual relationships among scholarly works in this field. The statistical population includes articles from 1991 to 2023 sourced from Noormags, Magiran, CIVILICA, SID, and the Comprehensive Portal of Humanities.
Findings reveal that CIVILICA has the highest number of publications, with 2022 marking the peak of scholarly output. The most frequent authorship pattern involved collaboration between two authors. Mohammad Reza Soroush emerged as the most prolific contributor. Most researchers held Master’s degrees, and Islamic Azad University had the largest share of contributors. The journal Teb-e Janbaz published the most articles, and psychology was the most active academic field.
In the network analysis, Mostafa Ghanei had the highest betweenness centrality, indicating a key role in information flow. The keyword “veterans” showed the highest degree centrality, making it the most prominent term. The psychology discipline and the University of Tehran were identified as the most influential field and institution, respectively.
Overall, scientific output in this area has increased steadily, reflecting growing academic interest. The presence of various disciplines among researchers suggests that the Holy Defense has been studied from diverse intellectual perspectives, emphasizing its broad relevance across the humanities and social sciences.