Objective: Iran’s publishing information ecosystem, consisting of actors, institutions, information resources, processes, and technological infrastructures, plays a key role in the production, organization, dissemination, and consumption of knowledge. However, the lack of an integrated framework for explaining relationships among its components has limited understanding of its interactions. This study aimed to semantically model Iran’s publishing information ecosystem through a knowledge graph and identify key knowledge nodes influencing the flow of book production, organization, distribution, and consumption.
Methodology: This applied qualitative study employed the systematic grounded theory approach of Strauss and Corbin. Data were collected through semi-structured interviews with 20 publishing experts and stakeholders, selected through purposive and snowball sampling until theoretical saturation was reached. Data analysis was conducted in MAXQDA using open, axial, and selective coding. To strengthen validity, ten years of statistical data on Iran’s publishing sector were also examined. Extracted concepts, knowledge nodes, and semantic relationships were organized into a knowledge graph and modeled using Microsoft Visio.
Findings and Conclusion: The results showed that Iran’s publishing information ecosystem has a dynamic, networked, and multilayered structure comprising ten semantic clusters. Knowledge graph analysis revealed that publishing governance and policymaking, publishing economics, reading culture, and digital infrastructures have the greatest centrality and influence. The knowledge graph also demonstrated strong potential for representing semantic structures, revealing hidden relationships, identifying key nodes, and supporting data-driven policymaking and the development of intelligent publishing infrastructures in Iran.