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@article{196410,
        author = {Elagandula Naresh Kumar and Lingam Narasimhulu and Dr. B. Chandrasekaram and Dr.R.J. Ramasree},
        title = {CATOLIBS: A Rule-Based System for Sanskrit Text Classification and Category Prediction Using Keyword Matching and Knowledge Base Retrieval},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3762-3766},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196410},
        abstract = {The classification and organization of Sanskrit literature is a challenging task due to the vast volume of textual data and the requirement of domain expertise. Manual classification is time-consuming and inefficient for large-scale digital collections. This work presents an implemented system named CATOLIBS (Classical and Traditional Organised Library Information Base System), designed to support the classification and organization of Sanskrit textual data. CATOLIBS is an academic implementation intended for research and educational purposes. The system follows a rule-based approach using keyword matching techniques to classify Sanskrit text into predefined categories such as Vedas, Upanishads, Epics, and other traditional classes. The system also integrates a structured knowledge base that stores information such as titles, authors, and descriptions, enabling efficient retrieval of relevant content and an interactive interface is implemented using Gradio, an open-source Python framework for creating simple web-based interfaces for user input and real-time output visualization. The proposed system provides a simple, interpretable, and efficient solution for Sanskrit text classification using a rule-based approach.},
        keywords = {Sanskrit Text Classification, Keyword Matching, Knowledge Base, Digital Library, Rule-Based NLP},
        month = {April},
        }