Smarter Knowledge Graph Extraction Without False Merges
See how ontology retrieval and model-free deduplication lifted recall to 95% without false entity merges.
30-Second TL;DR
What Changed
Retrieves only the relevant ontology slice from a graph database, reducing prompt catalog overhead by about 94%.
Why It Matters
The approach offers a practical blueprint for teams that need reliable knowledge graphs from messy enterprise archives, where inconsistent naming and duplicate relationships can undermine downstream search and analytics. Its no-false-merge guard is especially valuable in intelligence, compliance, and other high-stakes domains.
What To Do Next
Prototype a small document pipeline with Qwen3.5-9B, ontology-slice retrieval, and deterministic deduplication before evaluating model quality alone.
Key Points
- •Retrieves only the relevant ontology slice from a graph database, reducing prompt catalog overhead by about 94%.
- •Processes PDFs, spreadsheets, Office files, and images through format-specific handlers connected to Kafka document metadata.
- •Combines two-pass extraction with deterministic cleaning, chunk merging, relationship refinement, six model-free deduplication algorithms, and guarded entity resolution.
- •Corrected seven classes of silent quality defects, including truncated source text and duplicate entities caused by title prefixes.
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