Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval
Quick Answer
The proposed system automatically generates executable schemas from diverse data sources, enhancing knowledge graph construction and retrieval.
Quick Take
It outperforms retrieval-only and decomposition methods across four QA benchmarks, showcasing improved performance through schema-conditioned routing and structural intelligence.
Key Points
- Automatically discovers executable schemas from raw multi-source data.
- Improves performance over retrieval-only and decomposition baselines in QA benchmarks.
- Utilizes schema-conditioned routing for enhanced query-time retrieval.
- Incorporates structural analysis to infer identity and foreign keys.
- Supports multi-tool agent routing for diverse retrieval methods.
Paper Resources
Article Content
From source RSS / original summaryarXiv: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 -based schema discovery to attested fields; deterministic structural analysis infers identity keys, foreign keys, and source hierarchy; and the resulting schema drives extraction, deduplication, and cross-source linking into a provenance-aware knowledge graph.
At query time the schema -- optionally extended via a monotonic protocol -- conditions a multi-tool agent routing retrieval across structured lookup, graph traversal, and vector search, returning grounded answers with traceable citations.
In controlled zero-shot comparisons using the same LLM, data, and evaluation harness, the system improves over retrieval-only and decomposition-based baselines across four QA benchmarks, with ablations showing that schema-conditioned routing, structural intelligence, and schema-guided construction each contribute to the gains.
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