# The Impact of Modern AI in Metadata Management

arXiv: https://arxiv.org/abs/2501.16605 · HTML: https://mlsanity.com/paper/2501.16605/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 121 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Multi-Task Reinforcement Learning with Context-based Representations](https://mlsanity.com/paper/2102.06177/) (2021)
- [AgentSquare: Automatic LLM Agent Search in Modular Design Space](https://mlsanity.com/paper/2410.06153/) (2024)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [From Text to Insight: Large Language Models for Materials Science Data Extraction](https://mlsanity.com/paper/2407.16867/) (2024)
- [TapeAgents: a Holistic Framework for Agent Development and Optimization](https://mlsanity.com/paper/2412.08445/) (2024)
- [The role of metadata in reproducible computational research](https://mlsanity.com/paper/2006.08589/) (2020)
- [A Standardized Machine-readable Dataset Documentation Format for Responsible AI](https://mlsanity.com/paper/2407.16883/) (2024)
- [On the Automated Processing of User Feedback](https://mlsanity.com/paper/2407.15519/) (2024)
- [Data Mesh: a Systematic Gray Literature Review](https://mlsanity.com/paper/2304.01062/) (2023)
- [Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation](https://mlsanity.com/paper/2308.08378/) (2023)
- [Githru: Visual Analytics for Understanding Software Development History Through Git Metadata Analysis](https://mlsanity.com/paper/2009.03115/) (2020)
- [The W3C Data Catalog Vocabulary, Version 2: Rationale, Design Principles, and Uptake](https://mlsanity.com/paper/2303.08883/) (2023)
- [Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations](https://mlsanity.com/paper/2107.00516/) (2021)
- [Rule Based Metadata Extraction Framework from Academic Articles](https://mlsanity.com/paper/1807.09009/) (2018)
- [Quality Prediction of Open Educational Resources A Metadata-based Approach](https://mlsanity.com/paper/2005.10542/) (2020)
- [AI-Driven Frameworks for Enhancing Data Quality in Big Data Ecosystems: Error_Detection, Correction, and Metadata Integration](https://mlsanity.com/paper/2405.03870/) (2024)
- [Metadata Integration for Spam Reviews Detection on Vietnamese E-commerce Websites](https://mlsanity.com/paper/2405.13292/) (2024)
- [Participatory Approaches in AI Development and Governance: A Principled Approach](https://mlsanity.com/paper/2407.13100/) (2024)
- [A Generative AI-driven Metadata Modelling Approach](https://mlsanity.com/paper/2501.04008/) (2024)

## Built on by (most cited first, 1 of 1)

- [DatasetResearch: Benchmarking Agent Systems for Demand-Driven Dataset Discovery](https://mlsanity.com/paper/2508.06960/) (2025)

## Abstract (arXiv)

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.