FIBO in Neo4j: Applying Knowledge Graphs in the Financial Industry

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Dr. Jesús Barrasa discusses how knowledge graphs in Neo4j can help accelerate the adoption of financial industry standards like FIBO. Ontologies define shared vocabularies and are actionable knowledge fragments that can be used for insight, interoperability, and automation. FIBO models financial concepts and relationships, and Neo4j allows integrating this ontology to validate data constraints, infer new facts, and gain insights by exploring the financial domain model. Users are encouraged to start small by aligning their graph schema with FIBO to enable interoperability and get value from public ontologies.

Technology

FIBO in Neo4j: Applying
Knowledge Graphs in the
Financial Industry
Dr. Jesús Barrasa
Director Sales Engineering EMEA - Neo4j
@barrasadv

Neo4j can help accelerate the adoption
of public standards like FIBO in your
data architecture.
Ontologies are actionable knowledge
artifacts. You can get value from them
now with Neo4j.
Who is this talk for?

Advantages
Reduce duplication in app logic
Invest in a hugely important asset: Your data
Get extra insights into your domain
Repurpose enriched datasets

An Ontology is a shared, explicit,
formal domain model

Ontologies: Some popular ones
Financial Industry
General
Metadata/Education/Bibliogr.
Biological / Biomedical

What do ontologies look like? FIBO
https://spec.edmcouncil.org/fibo/
Stock
Corporation
rdf:SubClassOf
Privately
Held
Company
Board
Agreement
is Governed
By

What do ontologies look like? FIBO
How to (FIBO) model a non-for-profit
corporation’s jurisdiction?
MATCH fiboP = (corp:Class)-[*]-(jur:Class)
WHERE corp.name= "NotForProfitCorporation"
AND jur.name = "Jurisdiction"
RETURN fiboP

How are ontologies used?
1. Insight / Documentation: Human consumption
2. Interoperability: Ontologies deﬁne shared
vocabularies (dept-wide, enterprise-wide, public)
3. Automation: Ontologies are actionable
knowledge fragments
• Structural Integrity Validation
• Inferencing

Gain insights into a domain from visually
exploring an Ontology
Combine visual exploration with querying
Insight

Interoperability
Align Graph Schema with the
FIBO Ontology
Enable instant interoperability
with FIBO/RDF consuming apps

Automation: Constraint Validation
Translate / extend an ontology with
constraints (SHACL) to automate the
detection of violations and to keep
consistency.
(Run as batch for reports / apply transactionally)

Automation: Inferencing
Using general purpose methods that leverage
the explicit knowledge in an ontology.

$Q: I want all accounts for clients in NY in my Knowledge Graph MATCH (ac)-[:ACC_ZIP]->()-[:IN_STATE]->(:State { stateCode: ‘NY’}) WHERE ac:LoanOrCreditAccount OR ac:InvestmentAccount OR ac:TransactionDepositAccount OR ac:TimeDepositAccount ... RETURN ac.acctBalance, ac.acctId Automation: Inferencing$

$Q: I want all accounts for clients in NY in my Knowledge Graph Automation: Inferencing MATCH (ac)-[:ACC_ZIP]->()-[:IN_STATE]->(:State { stateCode: ‘NY’}) WHERE n10s.inference.hasLabel(ac,”Account”) RETURN ac.acctBalance, ac.acctId$

Takeaways / Call to action
Use Neo4j to get value from public
vocabularies like FIBO when building
your KG. You can do it now.
Start small and iterate. Be agile.
Reach out. We’d love to help.

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