Advanced Semantic Retrieval and Knowledge Management Systems Using Generative AI

Abstract

The rapid expansion of digital information has created significant challenges in efficiently retrieving and managing knowledge across organizations. Traditional keyword-based retrieval systems often fail to capture the contextual meaning and intent behind user queries, leading to suboptimal results. This paper presents an advanced semantic retrieval and knowledge management framework powered by Generative Artificial Intelligence (AI). The proposed system leverages large language models, vector embeddings, and transformer architectures to enhance contextual understanding and deliver more accurate and relevant search outcomes. By integrating semantic search with knowledge graphs and retrieval-augmented generation (RAG), the framework enables dynamic knowledge discovery and intelligent information synthesis. The system supports multiple data types, including structured databases, unstructured documents, and multimedia content, ensuring comprehensive knowledge accessibility. Furthermore, it incorporates scalable cloud-based infrastructure and automated pipelines for continuous learning and updating of knowledge repositories. Key challenges such as data heterogeneity, semantic ambiguity, and privacy concerns are addressed through advanced preprocessing, embedding techniques, and secure data handling mechanisms. Experimental evaluations demonstrate significant improvements in retrieval accuracy, user satisfaction, and decision-making efficiency. The proposed framework represents a next-generation solution for intelligent knowledge management systems, enabling organizations to harness the full potential of their data assets.