http://www.chinalibs.net 2025/2/13
[作者] Rajesh Kumar Das，Mohammad Sharif Ul Islam
[摘要] s the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed. Thirty-two papers were identified, analyzed and summarized on the application of AI and ML domain and techniques which are most often used. Findings show that The current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. For collection management in libraries, several Ml techniques like logistic regression, KNN, AdaBoost have been widely used for Metadata generation, resource discovery; and Book acquisition. Whereas for circulation (book recommendation, user rating, bibliographic data etc.) recommender system, SVM, association rule have been utilized. Library in-house activities like; cataloguing, classification, indexing, document analysis, text recognition etc., have been supported by both AI and ML technologies. Some advanced AI and ML techniques like pattern recognition and MAS are also being used to ensure library security; user identification; book title recognition; RFID management, and other administration activities. Deep learning, neural network algorithms, convolutional neural networks have also been proved as powerful tools for scholarship, collections discovery, search and analysis. Besides, an artificially intelligent conversational agent or chatbot works as a virtual reference librarian. It enhances face-to-face human interaction for library web site tour guides, automated virtual reference assistants, readers’ advisory-librarians, and virtual story-tellers. This study could help in the development of new ideas and models or tools to support and enhance the existing service ecologies of libraries. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways.
[关键词] artificial intelligence AI machine learning libraries systematic review
s the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed. Thirty-two papers were identified, analyzed and summarized on the application of AI and ML domain and techniques which are most often used. Findings show that The current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. For collection management in libraries, several Ml techniques like logistic regression, KNN, AdaBoost have been widely used for Metadata generation, resource discovery; and Book acquisition. Whereas for circulation (book recommendation, user rating, bibliographic data etc.) recommender system, SVM, association rule have been utilized. Library in-house activities like; cataloguing, classification, indexing, document analysis, text recognition etc., have been supported by both AI and ML technologies. Some advanced AI and ML techniques like pattern recognition and MAS are also being used to ensure library security; user identification; book title recognition; RFID management, and other administration activities. Deep learning, neural network algorithms, convolutional neural networks have also been proved as powerful tools for scholarship, collections discovery, search and analysis. Besides, an artificially intelligent conversational agent or chatbot works as a virtual reference librarian. It enhances face-to-face human interaction for library web site tour guides, automated virtual reference assistants, readers’ advisory-librarians, and virtual story-tellers. This study could help in the development of new ideas and models or tools to support and enhance the existing service ecologies of libraries. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways.
详情请下载：Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review.pdf
引用本文:
Rajesh Kumar Das，Mohammad Sharif Ul Islam．Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review[DB/OL]．[2025-04-30]．http://www.chinalibs.net/ArticleInfo.aspx?id=590291．
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