• Srinivas Sambari
• Chandra Chary Sreeramoj

The exponential growth of unstructured digital information has intensified challenges related to knowledge extraction, organization, retrieval, and decision support in knowledge-intensive environments. Traditional manual and rule-based knowledge management approaches often suffer from inefficiencies, low retrieval accuracy, and limited semantic understanding, particularly when processing large volumes of complex textual data such as research articles, technical reports, and organizational documents. In response to these limitations, this paper proposes an AI-driven knowledge mapping framework that integrates advances in natural language processing (NLP), large language models (LLMs), knowledge graphs, epistemic AI, and neurosymbolic AI to transform unstructured text into structured, actionable knowledge representations.The proposed framework systematically combines lexical, syntactic, and semantic NLP techniques—including keyword extraction, named entity recognition, relationship extraction, and semantic similarity analysis—to enable accurate knowledge identification and representation. Network-based analysis and graph querying mechanisms are employed to enhance knowledge integration, visualization, and retrieval within complex and interconnected information environments. By addressing inefficiencies observed in conventional hyperlink-based and keyword-driven retrieval systems, the framework aims to improve both retrieval speed and precision.Recognizing the need for trustworthy and ethically grounded AI systems, the study adopts a hybrid knowledge management perspective that balances AI automation with human contextual validation. This approach enhances knowledge accuracy, reduces bias, and supports high-quality decision-making while maintaining transparency and human oversight. The framework further facilitates the organization of both explicit and tacit knowledge, enabling the discovery of hidden insights within large textual corpora.This study contributes to the literature by synthesizing concepts from NLP, knowledge graph construction, complex network theory, and hybrid AI paradigms into a unified conceptual model for intelligent knowledge mapping. The proposed framework is applicable across diverse domains, including libraries, engineering, biomedical research, and organizational learning systems. Overall, the paper advances the development of scalable, efficient, and ethical AI-enabled knowledge mapping systems that support improved information retrieval, informed decision-making, and continuous learning in dynamic digital ecosystems.

AI-Driven Knowledge Mapping, Natural Language Processing, Knowledge Graphs, Hybrid Knowledge Management, Information Retrieval, Complex Information Environments.

"AI-Driven Knowledge Mapping in Complex Information Environments Using Hybrid NLP and Graph-Based Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 2, page no.275-280, February-2026, Available :http://www.jetir.org/papers/JETIRHJ06055.pdf

"AI-Driven Knowledge Mapping in Complex Information Environments Using Hybrid NLP and Graph-Based Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 2, page no. pp275-280, February-2026, Available at : http://www.jetir.org/papers/JETIRHJ06055.pdf