Utilizing Intelligent Language Processing and Computational Linguistics Models to enhance Knowledge-Based Systems
Abstract
This paper adopts a multi-method analytical approach to analyze and study the interoperability of intelligent language processing models in the context of knowledge-based systems. The purpose of this work is to examine semantic consistency and linguistic inference through structural equation modeling with bidirectional communication between language models and computational knowledge structures. Based on conceptual mapping, a dimensional classification instrument is developed and reclassified using regression-based reduction techniques to reduce the dimensionality. The tree-structured semantic format is used to analyze the rapid evolution of computational linguistics tools and the integrated expression of knowledge representation metrics. Through the factor analysis of each dimension, the problems related to semantic ambiguity and model scalability are investigated and variance-based analysis is conducted. Experiments have shown that when the lexical coherence index ranges from 0.52 to 0.87, the annotation time differs, but the search time remains less than 1.2 seconds. By applying a proposed hybrid inference model to the language-driven recommendation system, a dynamic feedback mechanism applicable to real-time semantic analysis is implemented. This tool may help to minimize the frequency of misinterpretation events and enable adaptive decision-making support for managing linguistic data streams.