Abstract :

Enterprise Knowledge Management (EKM) Has Become Increasingly Important For Organizations Seeking To Efficiently Capture, Organize, Retrieve, And Utilize Knowledge Assets Across Diverse Business Functions. Traditional Knowledge Management Systems Often Struggle With Information Fragmentation, Limited Contextual Understanding, And Inefficient Retrieval Mechanisms, Leading To Reduced Productivity And Decision-making Effectiveness. Recent Advancements In Large Language Models (LLMs) Provide New Opportunities To Transform Enterprise Knowledge Ecosystems Through Intelligent, Context-aware Information Processing. This Paper Presents An Intelligent Enterprise Knowledge Management Framework Utilizing Context-Aware LLM Architectures To Enhance Knowledge Discovery, Semantic Understanding, And Personalized Information Delivery. The Proposed Approach Integrates Contextual Reasoning, Natural Language Understanding, Semantic Search, And Adaptive Knowledge Retrieval To Provide Relevant And Accurate Responses Based On User Roles, Tasks, And Organizational Objectives. The Framework Employs Contextual Embeddings, Retrievalaugmented Generation, And Continuous Learning Mechanisms To Improve Knowledge Accessibility While Maintaining Information Consistency And Security. Experimental Analysis Demonstrates Significant Improvements In Retrieval Accuracy, Response Relevance, User Satisfaction, And Decision Support Capabilities Compared To Conventional Knowledge Management Solutions. The Proposed Architecture Enables Organizations To Leverage Enterprise Knowledge Effectively, Fostering Innovation, Collaboration, And Operational Excellence In Dynamic Business Environments.

Published:

16-12-2022

Issue:

Vol. 22 No. 12 (2022)

Page Nos:

81-89

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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