درخت حوزه‌های تخصصی

The current research aims to analyze the causes affecting the formation of the current state of inter-organizational knowledge (assets) sharing in the information and knowledge organizations located in Tehran smart city. Methodology: In terms of the purpose, this research is applied research with a descriptive nature, which was conducted with a mixed-method approach. In the qualitative part, the causes affecting the current state of inter-organizational knowledge sharing of organizations in the field of information and knowledge, such as the National Library and Archives of I.R. Iran, the Iranian Research Institute for Information Science and Technology (IranDoc), the organizations in charge of the public libraries and the university libraries of the country in Tehran was gathered through a semi-structured interview with 14 managers of information and knowledge organizations; In the quantitative part, the identified causes were validated using a researcher-made questionnaire using the Delphi technique (in two rounds) and the consensus opinion of 22 experimental experts. Data analysis has been done in the qualitative part by layered causal analysis and in the quantitative part using descriptive statistics indicators and Excel and SPSS software. Findings: The analysis of the data from the interviews led to the formation of 104 causes in the form of 39 causes in the litany layer, 25 causes in the systemic layer, 20 causes in the discourse layer, and 19 causes in the metaphor layer; After the second round of Delphi, 90 causes were validated in the form of 31 causes in the litany layer, 25 causes in the systemic layer, 19 causes in the discourse layer and 8 causes in the metaphor layer. In total, the causes of "Bipolar or hierarchical knowledge", in the metaphor layer and "inability of organizations to resolve conflict of interests" and "lack of necessary infrastructure" in the systemic layer had the highest mean; The causes of "selection of senior managers of information and knowledge centers based on political orientation", "lack of attention to becoming knowledge-based" and "viewpoint and approach of the organization leader" in the discourse layer had the next mean; The causes of "lack of common/centralized data and knowledge base and platform" and "non-implementation of knowledge management and inter-organizational communication" in the litany layer were ranked third in terms of the mean. Conclusion: In order to solve the problems and challenges of the current state, it is necessary to design and implement scenarios with the priority of creating discourse and changing the metaphors of the actors in this field, focusing on solving the problems caused by the bipolar or hierarchical knowledge metaphor and the discourses of the leadership approach of organizations, the selection of managers, and the knowledge-based approach.

Record linkage is vital for consolidating data from different sources, particularly in Persian records where diverse data structures and formats present challenges. To tackle these complexities, an expert system with decision tree algorithms is crucial for ensuring precise record linkage and data aggregation. Adaptation operations are created based on predefined rules by incorporating decision trees into an expert system framework, simplifying the aggregation of disparate data sources. This method surpasses traditional approaches like IF-THEN rules in effectiveness and ease of use and improves accessibility for non-technical users due to its intuitive nature. Integrating probabilistic record linkage results into the decision tree model within the expert system automates the linkage process, allowing users to customize string metrics and thresholds for optimal outcomes. The model’s accuracy rate of over 95% on test data highlights its effectiveness in predicting and adjusting to data variations, confirming its reliability in various record linkage scenarios. The innovative utilization of machine learning decision trees alongside probabilistic record linkage in an expert system represents a significant advancement in the field, providing a robust solution for data aggregation in intricate environments and large-scale projects involving Persian records. Combining decision tree algorithms and probabilistic record linkage within an expert system offers a powerful tool for handling complex data integration tasks. This approach not only streamlines the process of consolidating diverse data sources but also enhances the accuracy and efficiency of record linkage operations By leveraging machine learning techniques and automated decision-making processes, organizations can achieve significant improvements in data quality and consistency, paving the way for more reliable and insightful analytical results in implementing statistical registers. In conclusion, integrating decision trees and probabilistic record linkage in an expert system represents a cutting-edge solution for addressing data aggregation challenges in Persian records and beyond.