Disciplines

📚 Reference Intelligence (REFINT)

🔌 Sources for Reference Intelligence (3)

🎯 Mission Domains served

🎫 Data Points

🔍 Lookup

📊 Pre-built Queries · Reference Intelligence

🔄 Live Datasets & APIs (2 key-free · ingestible)

📜 Playbook — Reference Intelligence collection

1. Direction — frame the requirement for Reference Intelligence: what decision does this support, by when?
2. Collection — collect from the 3 mapped sources (2 free) — filter the catalog by REFINT; capture provenance and observe OPSEC.
3. Processing — normalize, de-duplicate and enrich the collected data.
4. Analysis — correlate against local holdings; apply ACH; assign confidence.
5. Dissemination — open a case, draft a report, share via STIX/MISP.
6. Feedback — set an alert rule / watchlist to monitor for change.

⚡ AI Skills & Automation

Automate unattended via the cron pipeline (collect → ingest → resolve → enrich → score → alert).

🗃 Data Management

🧩 Advanced Capabilities

🔗 Pivot to related disciplines

✨ Enrichment pathways

🎯 Mission

Reference Intelligence curates authoritative identifier and registry data to normalize, resolve, and cross-link entities across every other discipline. It answers what the canonical record and master identifier for an entity is, and how disparate datasets bind together through crosswalks between LEIs, tickers, registry numbers, and geonames.

📡 Collection methods

• Authoritative registry and standards-body ingestion
• Identifier crosswalk construction (LEI to ticker to CIK to registry number)
• Gazetteer and geoname reconciliation
• Entity resolution, deduplication, and record linkage
• Ontology and taxonomy mapping across schemas
• Data-provenance and lineage tracking
• Standards-code mapping (ISO, HS, NAICS/NACE)

📚 Key sources & datasets

🎫 Data points produced

🔧 Tools & frameworks

• OpenRefine
• Wikidata Query Service (SPARQL)
• Neo4j
• Dedupe.io
• Reconciliation API
• Python/pandas
• Apache Jena

📜 Reference Intelligence Tradecraft

1. Ingest authoritative registries and standards tables as canonical reference sets
2. Cleanse, standardize, and version records with provenance metadata
3. Reconcile incoming entities against master records using deterministic and probabilistic matching
4. Assign canonical identifiers and build crosswalks binding equivalent records together
5. Publish resolved reference tables and match confidences to downstream disciplines
6. Maintain freshness, retire stale identifiers, and flag unresolved entities for review

📊 Dashboard KPIs

🔍 Pre-built queries

🔗 Cross-discipline pivots

🗃 Data Management

🧩 Advanced Capabilities