Conceptual Schema Inference for Tabular Datasets using Large Language Models
📰 ArXiv cs.AI
Learn to infer conceptual schemas from tabular datasets using large language models to improve data understanding and organization
Action Steps
- Collect and preprocess tabular datasets from various sources
- Fine-tune a large language model on the preprocessed data to learn patterns and relationships
- Use the fine-tuned model to infer conceptual schemas from the data
- Evaluate and refine the inferred schemas using metrics such as accuracy and completeness
- Apply the inferred schemas to improve data discovery, exploration, and organization
Who Needs to Know This
Data scientists and data engineers can benefit from this technique to automate the process of deriving conceptual schemas from large collections of tabular data, making it easier to understand and organize data repositories
Key Insight
💡 Large language models can be used to infer conceptual schemas from tabular datasets, enabling automated data organization and discovery
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📊 Automatically infer conceptual schemas from tabular data using LLMs to improve data understanding and organization! #datascience #LLMs
Key Takeaways
Learn to infer conceptual schemas from tabular datasets using large language models to improve data understanding and organization
Full Article
Title: Conceptual Schema Inference for Tabular Datasets using Large Language Models
Abstract:
arXiv:2509.04632v2 Announce Type: replace-cross Abstract: Large collections of tabular data from data lakes, web tables and open data portals often originate from heterogeneous sources, leading to representational inconsistencies. Understanding and organizing such repositories therefore remains a major challenge. While prior work has primarily focused on dataset discovery and exploration, this paper addresses the complementary problem of conceptual schema inference: automatically deriving a conc
Abstract:
arXiv:2509.04632v2 Announce Type: replace-cross Abstract: Large collections of tabular data from data lakes, web tables and open data portals often originate from heterogeneous sources, leading to representational inconsistencies. Understanding and organizing such repositories therefore remains a major challenge. While prior work has primarily focused on dataset discovery and exploration, this paper addresses the complementary problem of conceptual schema inference: automatically deriving a conc
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