نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناسی ارشد مدیریت اطلاعات، گروه علم اطلاعات و مدیریت دانش، دانشکده مدیریت دولتی و علوم سازمانی، دانشکدگان مدیریت، دانشگاه تهران، ایران
2 استاد، گروه علم اطلاعات و دانششناسی، گروه علم اطلاعات و مدیریت دانش، دانشکده مدیریت دولتی و علوم سازمانی، دانشکدگان مدیریت، دانشگاه تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Purpose: Traditionally, knowledge organization systems—such as cataloging and classifying books, as well as document indexing—require precision, extensive subject knowledge, and adherence to classification standards and complex thesauri. Over the years, these processes have been meticulously designed to ensure quick and accurate access to scientific resources. However, performing these tasks requires a high level of expertise and skill from professionals, often making them labor-intensive. The advent of artificial intelligence (AI) is transforming these processes by introducing enhanced levels of efficiency and accuracy in knowledge organization, allowing tasks to be performed more precisely and effectively. The present study aims to assess the feasibility of employing artificial intelligence for semi-automating subject cataloging, book classification, and indexing tasks. Additionally, this research seeks to assess the performance and effectiveness of AI tools compared to traditional methods. Because it is not yet possible to organize knowledge in libraries completely automatically using artificial intelligence, machine learning, and deep learning, this requires AI to have access to the text of all books in libraries, which violates copyright law.
Method: Books were first examined in the National Library and Archives of Iran Catalogue. As a case study, ten Persian language books were selected, covering diverse topics to evaluate the effectiveness of artificial intelligence systems with greater precision. The titles of these books were then searched in the catalog of the National Library and Archives of Iran to determine the subject headings and classification codes (the Library of Congress Classification: LCC, and the Dewey Decimal Classification: DDC) assigned to each book. Three AI systems—ChatGPT, Copilot, and Gemini—were subsequently used for each book to assign subject headings, LCC numbers, and DDC numbers. The subject headings, LCC numbers, and DDC numbers generated by the AI systems were systematically compared with those assigned by the National Library. This comparison aimed to evaluate the degree of alignment and compatibility between traditional methods and modern AI tools, examining potential differences in their performance.
Findings: The results of the present study indicate a low level of alignment and compatibility between human catalogers and artificial intelligence in cataloging and classification. The subject headings, LCC numbers, and DDC numbers assigned by AI differ from those assigned by knowledge organization specialists and librarians. Nonetheless, ChatGPT, Copilot, and Gemini can be viewed as useful cataloging assistants and indexing tools that can be applied in cataloging, classification, and indexing tasks, playing a facilitative role in these processes.
Conclusion: Despite the noted differences between human catalogers and artificial intelligence tools, the use of artificial intelligence in knowledge organization can lead to a positive transformation by automating repetitive tasks, improving accuracy in classification and cataloging, and optimizing the process of creating metadata. AI technologies, as powerful assistants, have substantial potential to enhance efficiency and accuracy in knowledge organization and will play an increasingly important role in this field in the near future.
کلیدواژهها [English]
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