Speaker: Jesús Barrasa, Sales Engineering Director EMEA, Neo4j

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

2. 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?

3. Knowledge Graphs in Neo4j

4. Why Knowledge Graphs?

5. Knowledge Graphs are Explicit Knowledge

6. Knowledge Graphs are Explicit Knowledge

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

8. Ontologies

9. An Ontology is a shared, explicit,
formal domain model

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

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

12. What do ontologies look like? FIBO

13. 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

14. 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

15. Insight

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

17. Interoperability

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

19. Automation

20. 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)

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

22. 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

23. Automation: Inferencing

24. 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

25. 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.

26. Questions?