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New agent system automates data cataloging with LLMs · 1 source tracked

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Glyph, a multi-strategy agentic system designed to automate the description and tagging of enterprise data catalogs. This system uses cooperating LLM agents to generate column descriptions by grounding them in source code retrieved from GitHub via retrieval-augmented generation. Glyph also assigns sensitivity-ontology labels by running three parallel strategies—description tagging, line-of-business regex tagging, and metadata tagging—and fuses their outputs using Reciprocal Rank Fusion (RRF). A fine-tuned MiniLM encoder significantly improves metadata tagging accuracy, making the system auditable and operable as a production service. AI

IMPACT Automates critical data governance tasks, potentially improving data discovery and compliance in enterprises.

RANK_REASON The cluster describes a research paper detailing a new system and methodology for data cataloging. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New agent system automates data cataloging with LLMs · 1 source tracked

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The cluster describes a research paper detailing a new system and methodology for data cataloging. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sha Sundaram ·

    Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs

    Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present G…

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