Distributed Data Solutions for Knowledge Based Systems by using Computational Linguistics and Econometric modeling
Abstract
The development of distributed knowledge-based systems is of special significance to the realization of efficient data-driven decision-making frameworks. This research aims to develop a computational model to evaluate the factors that influence the intention of using linguistic and econometric modeling for knowledge systems, combining the variables found in the knowledge representation and econometric correlation model with other external variables such as data replication strategies, semantic coherence, syntax complexity, conflict resolution frequency, and encryption protocols. To obtain an accurate estimate, we propose a hybrid analytical approach based on regression correlation analysis and corpus-based NLP methods. Data compiled from distributed knowledge management companies in various industry sectors are used to test the formulated hypotheses. This article first analyzes the structural composition of distributed data frameworks, studies the causal effect of semantic complexity on the level of data retrieval efficiency of knowledge-based systems, and analyzes the moderating effect of conflict resolution frequency on the data availability of distributed knowledge repositories. The results of this study reflect what key linguistic and econometric factors should be considered and how they are interconnected in optimizing system performance. The results of this empirical analysis also provide a reference for further research on the scalability and resilience of distributed data solutions under the background of intelligent decision-support environments. Hence, we may conclude that our findings deserve more attention and practical application.