International Journal of Science, Engineering and TechnologyAuthors: Daniel Thompson, Dr. Olivia Harris, Charlotte Evans, Andrew Collins, Emily Carter, Jeji Krishnan
Abstract: Enterprise knowledge bases and issue management platforms generate large volumes of unstructured and semi-structured data from support tickets, incident reports, troubleshooting documents, and operational logs, making efficient knowledge extraction and issue analysis a major challenge in modern enterprise environments. This research paper presents an intelligent Natural Language Processing (NLP)-driven framework for Enterprise Knowledge Base Analytics and Issue Metadata Enrichment designed to improve knowledge discovery, issue classification, metadata accuracy, and operational decision-making. The proposed system utilizes advanced language intelligence techniques such as semantic analysis, contextual embeddings, entity recognition, topic modeling, automated metadata tagging, and similarity-based knowledge retrieval to transform raw enterprise content into structured and actionable insights. By integrating machine learning and deep learning models with enterprise support ecosystems, the framework enables automated categorization of incidents, detection of recurring issue patterns, intelligent recommendation of relevant knowledge articles, and enhanced search relevance across enterprise repositories. The research further explores metadata enrichment strategies that improve issue traceability, reduce manual annotation efforts, and support predictive analytics for proactive support operations. Experimental evaluation demonstrates that the proposed approach significantly enhances issue resolution efficiency, improves retrieval accuracy, and enables scalable real-time analytics within continuously evolving enterprise infrastructures. The findings emphasize the growing importance of AI-driven language intelligence in enterprise support engineering and knowledge management systems, contributing toward the development of intelligent enterprise ecosystems capable of automating knowledge extraction, improving operational visibility, and enabling data-driven operational optimization.
DOI: http://doi.org/10.5281/zenodo.20265224
