Natural Language Processing for IT Documentation and Knowledge Management

Gireesh Kambala

doi:10.18535/ijsrm/v11i02.ec3

Abstract

Natural Language Processing (NLP) is revolutionizing IT documentation and knowledge management by enabling automation in documentation generation, enhancing information retrieval, and improving knowledge structuring. Traditional IT documentation practices often face challenges such as unstructured content, redundancy, and difficulty in retrieving relevant technical information. NLP addresses these inefficiencies by leveraging advanced techniques like Named Entity Recognition (NER), Topic Modeling, and Transformer-based models to improve the accuracy and accessibility of IT knowledge repositories. This paper explores the applications of NLP in IT documentation, evaluates its impact on knowledge management, and discusses the challenges associated with its implementation, such as domain adaptation and integration with existing IT systems. A literature review provides an in-depth analysis of state-of-the-art NLP techniques, while case studies and empirical data illustrate the effectiveness of NLP-driven IT knowledge management systems. Furthermore, this paper highlights future research directions, including the fine-tuning of large language models (LLMs) for IT documentation and the integration of multi-modal AI approaches for enhanced contextual understanding and automation.

Keywords

Natural Language ProcessingIT DocumentationKnowledge ManagementNamed Entity

References

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[refR-14] Velardi, P., Faralli, S., & Navigli, R. (2013). Ontolearn reloaded: A graph-based algorithm for taxonomy induction. Computational Linguistics, 39(3), 665-707.Google Scholar ↗

[refR-15] Zhang, X., Guo, F., Chen, T., Pan, L., Beliakov, G., & Wu, J. (2023). A brief survey of machine learning and deep learning techniques for e-commerce research. Journal of Theoretical and Applied Electronic Commerce Research, 18(4), 2188-2216.Google Scholar ↗

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