Abstract:

Background: This study presents a thorough comparative analysis of national AI strategic policies and regulations on the economies of the most advanced AI nations using natural language processing (NLP) and knowledge graph (KG) techniques. By leveraging NLP and KG, our investigation systematically examines and compares the AI policies for AI leading nations to identify common themes, differences, and emerging trends: Purpose: The study aims to elucidate the strategic policy priorities and regulatory frameworks for AI leading nations that represents hope and a model for other nations. Significance: This study highlights the importance of NLP and KG as a powerful technique for policy analysis and provides insights into how AI leading nations approach AI governance to spearhead advance developmental issues. Practical implications: Knowledge graph represents an important task in data mining that assists in identifying database entries similarities, extracting information from large documents, and classifying text into organized content that can assist policy makers advance economic developmental knowledge from other nations. Method: This study utilizes Pearson correlation coefficient, natural language processing (NLP) and knowledge graph (KG) to analyze and compare the AI strategic policy regulations of the economies for leading AI nations. Findings: Based on the Pearson correlation coefficient, the study concluded that NLP and KG are great models needed for predicting and identifying AI strategic policies and regulatory similarities. Our investigated analysis reveals that there exists similarities and differences in the AI strategic policies and regulations which explains the linear economic developments from these nations.