Semantic Knowledge Graph Generation for AI-Driven Applications

Abstract

Semantic Knowledge Graphs (KGs) have emerged as pivotal components in enhancing AI systems by providing structured, context-rich representations of real-world entities and their interrelationships. This paper delves into the methodologies for generating semantic KGs, emphasizing their significance in AI applications such as natural language processing, recommendation systems, and decision support systems. We present a comprehensive literature survey highlighting existing techniques and challenges in KG generation, followed by an exploration of current systems and their limitations. Building upon this foundation, we propose an advanced framework that integrates large language models (LLMs) with semantic enrichment processes to automate and semantically enhance KG construction. Experimental results demonstrate the efficacy of our approach in improving the accuracy and scalability of KGs. The paper concludes by discussing the implications of our findings and suggesting directions for future research in the domain of semantic KG generation.