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The Impact of Modern AI in Metadata Management.

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Bibliographic Details
Title: The Impact of Modern AI in Metadata Management.
Authors: Yang, Wenli, Fu, Rui, Amin, Muhammad Bilal, Kang, Byeong
Source: Human-Centric Intelligent Systems; Sep2025, Vol. 5 Issue 3, p323-350, 28p
Subject Terms: ARTIFICIAL intelligence, AUTOMATION software, INFORMATION retrieval, CLASSIFICATION, DATABASES, TECHNOLOGICAL innovations, DESIGN, INFORMATION resources management
Abstract: Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Metadata management plays a critical role in data governance, resource discovery, and decision-making in the data-driven era. While traditional metadata approaches have primarily focused on organization, classification, and resource reuse, the integration of modern artificial intelligence (AI) technologies has significantly transformed these processes. This paper investigates both traditional and AI-driven metadata approaches by examining open-source solutions, commercial tools, and research initiatives. A comparative analysis of traditional and AI-driven metadata management methods is provided, highlighting existing challenges and their impact on next-generation datasets. The paper also presents an innovative AI-assisted metadata management framework designed to address these challenges. This framework leverages more advanced modern AI technologies to automate metadata generation, enhance governance, and improve the accessibility and usability of modern datasets. Finally, the paper outlines future directions for research and development, proposing opportunities to further advance metadata management in the context of AI-driven innovation and complex datasets. [ABSTRACT FROM AUTHOR]
ISSN:26671336
DOI:10.1007/s44230-025-00106-5