Thesis Abstract

Abstract
The rapid growth of digital information and resources in libraries has led to challenges in cataloging and classifying materials effectively. This thesis explores the application of Artificial Intelligence (AI) in improving library cataloging and classification processes. The research focuses on developing AI algorithms and technologies to enhance the accuracy, efficiency, and consistency of these critical library functions. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Definitions of key terms related to AI, library cataloging, and classification are also outlined to establish a common understanding. Chapter Two consists of a comprehensive literature review that examines existing studies, methodologies, and technologies related to AI in library science. The review covers topics such as machine learning, natural language processing, data mining, and knowledge representation in the context of cataloging and classification in libraries. Chapter Three details the research methodology employed in this study. It includes discussions on research design, data collection methods, AI algorithms selected for experimentation, implementation strategies, evaluation criteria, and ethical considerations. The chapter also outlines the process of data analysis and interpretation. Chapter Four presents the findings of the research, highlighting the outcomes of implementing AI technologies in library cataloging and classification. The discussion covers the effectiveness of AI algorithms in improving accuracy and efficiency, the impact on workflow processes, and the challenges encountered during implementation. Case studies and examples are provided to illustrate the practical application of AI in library settings. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and suggesting future directions for further exploration and development in this field. The conclusion emphasizes the potential of AI to revolutionize library cataloging and classification practices, leading to enhanced information retrieval, user experience, and overall library services. In conclusion, this thesis contributes to the growing body of knowledge on the utilization of AI for improved library cataloging and classification. By leveraging AI technologies, libraries can streamline operations, increase the accuracy of metadata assignment, and provide more personalized and efficient access to information resources for users. This research lays the foundation for future advancements in AI applications within the field of library and information science.

Thesis Overview

The project titled "Utilizing Artificial Intelligence for Improved Library Cataloging and Classification" aims to explore the application of artificial intelligence (AI) in enhancing the processes of cataloging and classifying resources in libraries. In traditional library settings, cataloging and classification are manual tasks performed by librarians, which can be time-consuming and prone to human error. By leveraging AI technologies, such as machine learning and natural language processing, this research seeks to automate and streamline these processes, ultimately improving the efficiency and accuracy of library cataloging and classification systems. The research will begin with a comprehensive literature review to examine existing studies, tools, and technologies related to AI in library and information science. This review will provide a foundation for understanding the current landscape and identifying gaps in the literature that this research aims to address. The methodology chapter will outline the research design and approach, including data collection methods, AI algorithms to be utilized, and evaluation criteria for assessing the effectiveness of the proposed AI-based cataloging and classification system. The research will involve collecting a dataset of library resources, developing AI models for cataloging and classification tasks, and evaluating the performance of these models against traditional manual methods. The findings chapter will present the results of the experiments conducted, highlighting the strengths and limitations of the AI-based system compared to manual cataloging and classification processes. The discussion will analyze the implications of these findings for library management and propose recommendations for implementing AI technologies in library settings. In conclusion, this research will contribute to the field of library and information science by demonstrating the potential benefits of AI in improving library cataloging and classification practices. By automating routine tasks and enhancing the accuracy of resource organization, AI technologies have the potential to revolutionize how libraries manage and provide access to information resources.