Impact and influence of modern AI in metadata management

2025-01-28

AI Summary

Traditional metadata management suffers from low automation, reactive governance, weak semantic expressiveness, and heavy reliance on manual effort. To address these challenges, this paper proposes the first AI-augmented metadata management framework integrating large language models (LLMs), knowledge graphs, and automated semantic understanding. The framework enables intelligent metadata generation, dynamic governance, and semantic enrichment—overcoming limitations of static schemas and manual annotation. By orchestrating open-source and commercial toolchains, it achieves unified modeling and real-time evolution of heterogeneous, cross-source data. Experimental results demonstrate a 32% improvement in metadata generation accuracy and a 4.8× increase in coverage; governance cycle time is reduced by 73%; and discoverability and interoperability of complex datasets are significantly enhanced. This work establishes a scalable, production-ready paradigm for intelligent data governance in data-intensive environments.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Artificial Intelligence
• Metadata Management
• Data Intensive Age

Innovation

• Artificial Intelligence
• Metadata Generation
• Data Management Efficiency