What does this research mean for the field?

The Prioris pipeline architecture enables the creation of evidence-graded knowledge graphs through a comprehensive system for source ingestion, deduplication, and confidence calibration. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to present a complete architecture for processing and delivering knowledge graphs from various data sources.

March 16, 2026Open Access

End-to-End Pipeline Architecture for Evidence-Graded Knowledge Graphs: From Source Ingestion to Weighted Edge Delivery

Key Points

The aim is to present a complete architecture for processing and delivering knowledge graphs from various data sources.
Implemented three-channel ingestion for diverse data sources.
Utilized two-layer deduplication and fact extraction methods.
Adopted PostgreSQL+pgvector for data modeling.
Integrated Docker for reproducible deployment.
Monitored data staleness to ensure up-to-date information.
Achieved efficient ingestion of 271 source definitions from 25 domains.
Enhanced data accuracy through confidence calibration and composite weighting.
Established a robust architecture for systematic data processing.

Abstract

Paper 6 in the Prioris research series. Describes the complete Prioris pipeline architecture: three-channel ingestion (253 RSS, 11 API, 7 Playwright), two-layer deduplication, fact/observation/inference extraction, confidence calibration, and composite weighting. Covers 271 source definitions across 25 domains, PostgreSQL+pgvector data model, staleness monitoring, and Docker-based reproducible deployment. Systems paper — architecture description, not empirical benchmark. Part of the research programme (Paper 0: 10.5281/zenodo.19024611).

End-to-End Pipeline Architecture for Evidence-Graded Knowledge Graphs: From Source Ingestion to Weighted Edge Delivery

Key Points

Abstract

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