FollowTheMoney Ontology¶

The FollowTheMoney (FTM) schema is a standard for representing entities and relationships in investigative journalism, anti-corruption research, and sanctions tracking. This page explains its structure and how it's used in the terrorist organizations use case.

What is FollowTheMoney?¶

FollowTheMoney is an open-source data model created by the Organized Crime and Corruption Reporting Project (OCCRP) and maintained as part of the OpenSanctions project. It provides:

• Entity-relationship model for people, companies, and their connections
• Standard properties for names, identifiers, dates, and locations
• Relationship types for ownership, employment, sanctions, and more
• Multi-source integration across jurisdictions and data providers

Origin and Adoption¶

Users: Investigative journalists, compliance teams, law enforcement, intelligence analysts, and researchers worldwide.

Ontology Structure¶

FTM organizes entities in a hierarchy with Thing as the root:

Thing (root)
├── LegalEntity
│   ├── Person
│   ├── Organization
│   │   ├── Company
│   │   └── PublicBody
│   └── Vehicle
│       └── Airplane
├── Asset
│   ├── BankAccount
│   ├── RealEstate
│   ├── Security
│   └── CryptoWallet
├── Document
│   ├── Article
│   ├── Passport
│   └── Email
└── Address

Relationship Classes (Intervals)¶

FTM models relationships as first-class entities:

Interval (relationship base)
├── Ownership    (owner → asset)
├── Directorship (director → organization)
├── Employment   (employee → employer)
├── Sanction     (authority → target)
├── Family       (person ↔ relative)
├── Associate    (person ↔ associate)
├── Membership   (member → organization)
└── Representation (representative → client)

This reification allows relationships to have their own properties (dates, sources, amounts).

Key Classes for Sanctions¶

Person¶

Individuals subject to sanctions or associated with designated entities.

ftm:Person a rdfs:Class ;
    rdfs:label "Person" ;
    rdfs:comment "A natural person" ;
    rdfs:subClassOf ftm:LegalEntity .

Properties: - name, firstName, lastName - birthDate, birthPlace - nationality, country - passportNumber, idNumber - alias (alternative names)

Organization¶

Groups, companies, and formal entities.

ftm:Organization a rdfs:Class ;
    rdfs:label "Organization" ;
    rdfs:comment "A formal organization" ;
    rdfs:subClassOf ftm:LegalEntity .

Properties: - name, alias - country, jurisdiction - classification (type of organization) - topics (e.g., "sanction", "crime.terror")

PublicBody¶

Government agencies and authorities.

ftm:PublicBody a rdfs:Class ;
    rdfs:label "Public body" ;
    rdfs:comment "A government agency or state-controlled entity" ;
    rdfs:subClassOf ftm:Organization .

Used for: - US State Department - UK Home Office - Australia Home Affairs - Other designating authorities

Sanction¶

The designation relationship between authority and target.

ftm:Sanction a rdfs:Class ;
    rdfs:label "Sanction" ;
    rdfs:comment "A sanction or listing of an entity by an authority" ;
    rdfs:subClassOf ftm:Interval .

Properties: - entity (target of sanction) - authority (issuing body) - program (e.g., "US-FTO219", "UK-PROSCRIBED") - startDate, endDate - reason, summary

Class Statistics¶

The full FTM ontology contains:

Classes Used in Terrorist Orgs¶

Aletheia Extensions¶

The terrorist organizations use case extends FTM with additional relationship types:

HAS_ALIAS Extension¶

FTM stores aliases as properties, but graph queries need traversable relationships:

ftm:HasAlias a rdfs:Class ;
    rdfs:label "Has Alias" ;
    rdfs:comment "Links an entity to an alternative name" ;
    rdfs:subClassOf ftm:Interval .

Enables queries like:

// Find organization by alias
MATCH (org:Organization)-[:HAS_ALIAS]->(a)
WHERE a.name = 'LeT'
RETURN org.name
// → Lashkar-e-Taiba

Extension file: ontology/extensions/aletheia_extensions.ttl

Example Entities¶

Organization with Aliases¶

{
  "schema": "Organization",
  "id": "org-hamas",
  "properties": {
    "name": ["Hamas"],
    "alias": ["Islamic Resistance Movement", "Harakat al-Muqawama al-Islamiya"],
    "country": ["ps"],
    "topics": ["sanction", "crime.terror"]
  }
}

Graph representation:

(Hamas:Organization)
    ├──[:HAS_ALIAS]──► (Islamic Resistance Movement:Entity)
    ├──[:HAS_ALIAS]──► (Harakat al-Muqawama:Entity)
    └──[:SANCTION]──► (US State Department:PublicBody)

Multi-Jurisdiction Sanction¶

                    ┌──[:SANCTION]──► (US State Department)
                    │
(Hizballah) ────────┼──[:SANCTION]──► (UK Home Office)
                    │
                    └──[:SANCTION]──► (Australia Home Affairs)

Using FTM in Aletheia¶

Loading the Ontology¶

# Load FTM ontology to graph
aletheia build-ontology-graph \
  --use-case terrorist_orgs \
  --knowledge-graph ftm_ontology

Building Knowledge Graph¶

aletheia build-knowledge-graph \
  --use-case terrorist_orgs \
  --knowledge-graph terrorist_orgs \
  --schema-mode graph-hybrid \
  --ontology-graph ftm_ontology

Viewing the Graph¶

# Count by entity type
redis-cli GRAPH.QUERY terrorist_orgs \
  "MATCH (n) RETURN labels(n), count(*) ORDER BY count(*) DESC"

# View sanctions relationships
redis-cli GRAPH.QUERY terrorist_orgs \
  "MATCH (org:Organization)-[:SANCTION]->(auth:PublicBody)
   RETURN org.name, auth.name LIMIT 10"

Semantic Alignment Examples¶

The ontology enables consistent extraction:

Benefits for Sanctions Analysis¶

1. Cross-Jurisdiction Queries¶

Single query finds all designations:

MATCH (org:Organization)-[:SANCTION]->(auth:PublicBody)
WHERE org.name CONTAINS 'Hamas'
RETURN auth.name
// → US State Department, UK Home Office, Australia Home Affairs

2. Alias Resolution¶

Find organizations by any known name:

MATCH (org:Organization)-[:HAS_ALIAS]->(a)
WHERE a.name =~ '(?i).*lashkar.*'
RETURN DISTINCT org.name
// → Lashkar-e-Taiba

3. Network Analysis¶

Trace organizational relationships:

MATCH path = (org:Organization)-[:RELATES_TO*1..3]-(related)
WHERE org.name = 'al-Qaeda'
RETURN path

4. Compliance Integration¶

FTM data exports directly to compliance systems: - Standardized entity identifiers - Authority-specific program codes - Machine-readable sanctions lists

Source Files¶

Data Sources¶

The terrorist organizations data comes from OpenSanctions:

Further Reading¶

• FollowTheMoney Documentation
• OpenSanctions Datasets
• Ontology Integration Guide
• Terrorist Organizations Demo