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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 11·5 min read
Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction
TL;DR
Doc2DB-Bench is a new benchmark designed for converting complex documents into relational databases, addressing limitations of existing Document-to-Table methods. It features 203 document instances across various schemas, enabling better evaluation of document understanding in database construction.
✦ Why It Matters
Start using Doc2DB-Bench to improve your document-to-database conversion processes today.
Key Takeaways
- Utilize Doc2DB-Bench for testing document-to-database conversion models.
- Focus on relational integrity when designing AI systems for data extraction.
- Explore the 203 document instances to enhance model training.
- Verify the authenticity of generated documents against real-world data.
Full Summary
As AI systems increasingly need to transform diverse documents into structured, queryable relational databases, existing benchmarks fall short by oversimplifying data into single tables. This can lead to issues like entity duplication and loss of complex relationships.
Doc2DB-Bench addresses these challenges by providing a comprehensive benchmark with 203 long-document instances, organized into 42 schemas and seven domain groups. It features 117 entity tables and 132 relationship tables, totaling 7,341 rows and 41,935 cells.
The benchmark was created using a controllable database-to-document synthesis pipeline and is verified for authenticity against real-world references. By focusing on relationally faithful document-to-database construction, Doc2DB-Bench serves as a valuable resource for developing reliable AI systems that can handle complex data structures.