arXiv:2606.05415v1 Announce Type: new Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inconsistent schemas and formats, yet existing approaches either demand costly manual engineering or bypass structure entirely. We present a system that automatically discovers an executable schema from raw multi-source data and uses it as a shared contract for knowledge graph construction and query-time retrieval. A closed-world field catalog constrains LLM-based schema discovery to attested fields;
The proliferation of disparate data sources and the increasing capabilities of large language models necessitate automated solutions for data integration and retrieval.
This development addresses a fundamental challenge in leveraging diverse real-world data, enabling more efficient knowledge graph construction and advanced query capabilities crucial for AI agent development.
The ability to automatically discover executable schemas from multi-source data significantly reduces manual engineering effort and improves the accuracy and breadth of integrated information systems.
- · AI software developers
- · Enterprises with complex data landscapes
- · Data scientists
- · Knowledge graph vendors
- · Manual data integration consultants
- · Companies relying on siloed data systems
- · Traditional schema definition tools
Automated data integration accelerates the development and deployment of sophisticated AI applications and agents.
Reduced data preparation overhead could enable smaller teams to build complex AI systems, democratizing advanced AI development.
The proliferation of autonomously integrated data could lead to emergent AI capabilities across previously disconnected domains, fostering new industries or disrupting existing ones.
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Read at arXiv cs.CL