LLMs Enhance Data Lake Metadata for Relationship Discovery
Key takeaways
- LLMs can significantly improve metadata quality and relationship discovery in data lakes.
- ColRel uses a two-stage method, including business dictionaries, to interpret complex schema labels.
- The approach is particularly effective for ERP-derived datasets with weak semantic signals.
- Automating metadata enrichment can reduce manual effort and accelerate data integration.
Who benefits
Summary
This paper introduces ColRel, a two-stage method that uses large language models and business dictionaries to build column embeddings from metadata and data, improving relationship discovery in data lakes, especially for complex ERP datasets. Experiments show its effectiveness in semantically related, weak-signal settings.
Why it matters
Professionals managing large data estates can leverage this approach to improve data discoverability and integration, reducing manual effort in understanding complex datasets and accelerating data-driven initiatives.
How to implement this in your domain
- 1Evaluate current data lake metadata quality and identify gaps in column relationship discovery.
- 2Explore integrating business dictionaries or glossaries to enrich existing metadata.
- 3Pilot a two-stage embedding process using LLMs to generate semantic descriptions for data columns.
- 4Develop automated tools to apply this method at data ingestion time for new datasets.
- 5Measure the improvement in data discoverability and the efficiency of data integration projects.
Original post by Ahlame Diouan (ERIC, UL2), Eric Ferey (ERIC, UL2), Sabine Loudcher (ERIC, UL2), J\'er\^ome Darmont (ERIC, UL2)
"arXiv:2608.26750v1 Announce Type: new Abstract: Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in ERP-derived datasets with coded or abbreviated schema labels. We propose ColRel…"
View on XOriginally posted by Ahlame Diouan (ERIC, UL2), Eric Ferey (ERIC, UL2), Sabine Loudcher (ERIC, UL2), J\'er\^ome Darmont (ERIC, UL2) on X · view source
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