1. Conceptual Ontology Engineering Tutorial Session 5: Putting it to Work
Hypercube Ltd.
Conceptual Ontology Engineering Tutorial Session 5: Putting it to Work
Mike Bennett
FOIS – JOWO
Cape Town, South Africa
September 2018

2. Session 5: Putting it to Work
An example conceptual ontology: the Financial Industry Business Ontology (FIBO)
from the Enterprise Data Management Council
Extending and deploying conceptual ontology
Example usage scenarios
Deterring the appropriate ontology styles
Corporate transformation with ontology

3. Introducing FIBO
FIBO is a collaborative effort among industry practitioners, semantic technology experts and information scientists to standardize the language used to precisely define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and the legal obligations and process aspects of corporate actions.   FIBO is being released as a series of standards under the technical governance of the Object Management Group (OMG):
FIBO is based on the legal structures and obligations contained within the myriad of contracts that form the foundation of the financial industry. We have constructed this content into both a business conceptual and fully operational ontology that formally models the reality of how the financial industry operates. 
The EDM Council is the author and steward of the Financial Industry Business Ontology (FIBO). 
Copyright © 2010 EDM Council Inc.

4. Financial Industry Business Ontology (FIBO)
FIBO is a business conceptual model that precisely describes financial instruments, pricing, legal entities and financial processes (what they are and how they work)
FIBO facilitates data harmonization across disparate repositories based on legal meaning and contractual obligation
FIBO is built on state-of-the-art collaboration technology and supported by documented and tested governance
FIBO is expressed in the W3C standard (RDF/OWL) for flexible and scenario-based/inference analysis
FIBO provides structural validation to ensure completeness, consistency and allowable values
FIBO feeds analytical processes with trusted data and powers smart contracts

5. Debt Securities Sample Hierarchy: Bonds

6. Corporate Bonds Overview

7. Examples: Bonds Foundational concepts for Contracts
Terms common to all debt
Interest, Principal terms
common to Bonds
Terms specific to Municipal Bonds

8. Review Formats – 1. Basic SME View
Boxes and lines – draw a line between kinds of thing
This is a characteristic or “property” of that kind of thing
Defines a feature of something in terms of its relation to something else
There are also properties defined in terms of simple assertions (yes/no, text, numbers)
Also show what kind of thing something is (classification hierarchy)
Term
Definition
Synonyms
Necessary Conditions?
bond
A debt security, in which the authorized issuer owes the holders a debt and, depending on the terms of the bond, is obliged to pay interest (the coupon) and/or to repay the principal at a later date, termed maturity. It is a formal contract to repay borrowed money with interest at fixed intervals.
has principal repayment terms
Repayment terms for the principal on a bond
has redemption terms
must be some
bond principal repayment terms set
Terms for the repayment of the Principal on a Bond security.

9. Reviews Formats 2. Additional Semantics Representation
Ask what it takes for something to be a member of a particular category of thing
E.g. “Is it necessarily the case that…”
This is added to the ontology by the semantics gurus
This uses a notation called SIMF
Logic and multiplicities corresponding to OWL semantics “restrictions”

10. FIBO: Scope and Content
Upper Ontology
FIBO Foundations: High level abstractions
FIBO Business Entities
FIBO Financial Business and Commerce
FIBO Indices and Indicators
FIBO Contract Ontologies
Securities (Common, Equities)
Securities (Debt)
Derivatives
Loans, Mortgage Loans
Funds
Rights and Warrants
FIBO Pricing and Analytics (time-sensitive concepts)
Pricing, Yields, Analytics per instrument class
FIBO Process
Corporate Actions, Securities Issuance and Securitization
Future FIBO: Portfolios, Positions etc.
Concepts relating to individual institutions, reporting requirements etc.

11. Using and Extending FIBO
Firm’s Business Conceptual Ontology
DEPLOY
There are two dimensions to the use of the FIBO ontology standard:
Deploying it in the IT environment
Extending with the firm’s own concepts
However….
App

12. Using and Extending FIBO
Firm’s Business Conceptual Ontology
DEPLOY
However….
The conceptual ontology is the firm’s own resource and should be extended with firm-specific concepts before being used in deployment.
App

13. Deploying Extended BCO
Customer BCO
Adapters
DEPLOY
DEPLOY
Common Logical Data Model
Operational Ontologies
Operational Ontologies
Operational Ontologies
Local LDMs
Local LDMs

14. Deploying Extended BCO
External Foundations semantics Conceptual Ontology Logical data model from Logical Ontology Internal Correspondence semantics Logical Ontology
Customer BCO
Adapters
DEPLOY
DEPLOY
Common Logical Data Model
Operational Ontologies
Operational Ontologies
Operational Ontologies
Local LDMs
Local LDMs

15. Mapping and Data Integration
Not as simple as that…
One concept may be modelled in different data element combinations
One data element may refer to a whole path through the ontology graph
Conceptual model must be general enough that a range of different logical designs can be referenced to it
E.g. stand up wide range of Relative, Occurrent etc along with property chains and intermediate classes
© Hypercube 2015
How to Buiild a Canonical Reference Ontology

16. Ontology to Data Model

17. Data Model to Ontology

18. Querying across Legacy Data Sources
Recommended Architectures
Wrappers and Adapters
When to stand up a triple store
© Hypercube 2015
How to Buiild a Canonical Reference Ontology

19. Challenges Triple store and Data Management
Timeliness (how often to update)
Provenance
System of record
Data lineage
What if we could use the ontology to report on the data in situ?

20. Virtual Ontology Reference ontology Legacy Data Sources and Systems
Reporting
Risk, Compliance etc.
Semantic Queries
Reference ontology
R2RML based Ontology to Legacy Database Adapters
Legacy Data Sources and Systems

21. Virtual Ontology Reference ontology Legacy Data Sources and Systems
Logical Ontology
Foundational semantics
Reporting
Risk, Compliance etc.
Semantic Queries
Reference ontology
R2RML based Ontology to Legacy Database Adapters
Legacy Data Sources and Systems

22. Reasoning with Semantic Web
Logical Ontologies – Design Guidelines
Stand-alone ontology design techniques and practice
What works with an enterprise conceptual ontology and what doesn’t?
Striking the balance!
© Hypercube 2015
How to Buiild a Canonical Reference Ontology

23. Semantic Web Applications
Semantic Operational Processing Reasons over Data to Infer Classifications and Relationships
isTradingWith is a new property relationship that is inferred based on a semantic rule and can be queried
An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments.
Human Facing Definition
Swap_Contract and
hasLeg FixedRateLeg and
hasLeg FloatingRateLeg
Machine Facing Definition
Fixed Float IR Swap (Ontology)
isTradingWith
Business Entity
Business Entity
Interest Rate Swap
Acme Inc
Trader LLC 4
Semantic reasoning
type
Inferred
Swap is inferred to be a Fixed-Float IR Swap because one leg was inferred to be fixed and one leg was inferred to be floating fulfilling the definitions in the ontology
identifies
Fixed Float IR Swap
identifies
type
Inferred
LEI5001
Swap1001
LEI7777
LEI
hasLeg
party
hasLeg
Swap
party
LEI 3
Semantic reasoning
FloatingRateLeg
Inferred
Leg 1
Leg 2
Inferred
FixedRateLeg
type
type
notional
index
fixedRate
notional
Leg1 is inferred to be a FloatingRateLeg because any leg tied to an index is semantically defined as floating
LIBOR
3.5%
Leg2 is inferred to be a FixedRateLeg because any leg tied to an interest rate is semantically defined as fixed
currency
currency
USD
Data for an undefined Swap Contract before semantic reasoning performs classification and identification 1
Semantic reasoning
USD 2
Semantic reasoning
David Newman, Wells Fargo

24. Semantic Web Applications
Physical Ontology
Internal Consistency semantics (reasoning)
isTradingWith is a new property relationship that is inferred based on a semantic rule and can be queried
An interest rate swap in which fixed interest payments on the notional are exchanged for floating interest payments.
Human Facing Definition
Swap_Contract and
hasLeg FixedRateLeg and
hasLeg FloatingRateLeg
Machine Facing Definition
Fixed Float IR Swap (Ontology)
isTradingWith
Business Entity
Business Entity
Interest Rate Swap
Acme Inc
Trader LLC 4
Semantic reasoning
type
Inferred
Swap is inferred to be a Fixed-Float IR Swap because one leg was inferred to be fixed and one leg was inferred to be floating fulfilling the definitions in the ontology
identifies
Fixed Float IR Swap
identifies
type
Inferred
LEI5001
Swap1001
LEI7777
LEI
hasLeg
party
hasLeg
Swap
party
LEI 3
Semantic reasoning
FloatingRateLeg
Inferred
Leg 1
Leg 2
Inferred
FixedRateLeg
type
type
notional
index
fixedRate
notional
Leg1 is inferred to be a FloatingRateLeg because any leg tied to an index is semantically defined as floating
LIBOR
3.5%
Leg2 is inferred to be a FixedRateLeg because any leg tied to an interest rate is semantically defined as fixed
currency
currency
USD
Data for an undefined Swap Contract before semantic reasoning performs classification and identification 1
Semantic reasoning
USD 2
Semantic reasoning
David Newman, Wells Fargo

25. Regulatory Reporting
Use granular semantics for reporting to regulators, central banks
Flexible access to data via separate semantic “layer”
Semantic querying
Fast turn-around of new reporting requirements
Meets BCBS239 RDA requirement for flexible, timely reporting

26. Regulatory Reporting Current State?
Reports (forms)
FORMS
FORMS
REPORTING ENTITY
REGULATORY AUTHORITY

27. Regulatory Reporting Current State?
Reports (forms)
FORMS
FORMS
REPORTING ENTITY
REGULATORY AUTHORITY
Change in Reporting requirements =
Redevelopment effort
By each reporting entity
For each system and form
Uncertainty
Content of the reports
Are we comparing like with like?
Data quality and provenance

28. Regulatory Reporting with Semantics!
Ontology
Thing
IR Swap
CDS
Bond
Contract
Granular
data
Thing
IR Swap
CDS
Bond
Contract
Common
ontology
Common
ontology
REPORTING ENTITY
REGULATORY AUTHORITY
Data is mapped from each system of record into a common ontology
Reported as standardized, granular data
Agnostic to changes in forms
Receives standardized, granular data aligned with standard ontology (FIBO)
Uses semantic queries (SPARQL) to assemble information
Changes to forms need not require re- engineering by reporting entities

29. Regulatory Reporting with Semantics
Conceptual Ontology
Foundational semantics !
Ontology
Thing
IR Swap
CDS
Bond
Contract
Granular
data
Thing
IR Swap
CDS
Bond
Contract
Common
ontology
Common
ontology
REPORTING ENTITY
REGULATORY AUTHORITY
Data is mapped from each system of record into a common ontology
Reported as standardized, granular data
Agnostic to changes in forms
Receives standardized, granular data aligned with standard ontology (FIBO)
Uses semantic queries (SPARQL) to assemble information
Changes to forms need not require re- engineering by reporting entities

30. Ontology for Blockchain

31. Ontology for Blockchain
Conceptual Ontology (legal)
Foundational semantics
Physical API

32. Graph Analytics to Describe Risk
Score = eigenvector centrality of adjacent network positions
SPARQL interface with R “igraph” package
Line width reflects aggregate amounts at risk between individual trading parties
Node size reflects grand total aggregate amount at risk for entity

33. Getting to There
Use of Semantics

34. Artifacts Analysis See OntoClean (Guarino and Welty)
Other analysis frameworks
OOPS!
OQuaRE (derived from SQuaRE)
Ontology Hackathon unification of these

35. Deployment Roadmap Knowledge and Meaning
Information Technology and Data
Is of
Data Dictionary
Is of
Is of
Physical Data Dictionary

36. Information Technology and Data
Deployment Roadmap
Knowledge and Meaning
Conceptual
Linguistic
Information Technology and Data
Is of
Data Dictionary
Is of

37. Information Technology and Data
Deployment Roadmap
Knowledge and Meaning
Conceptual
Linguistic
Information Technology and Data
Is of
Data Dictionary
Is of

38. Deployment Roadmap Knowledge and Meaning By reference
Conceptual ontology
Knowledge and Meaning
By reference
Semantic Data Model
Information Technology and Data
Is of
Data Dictionary
Is of
Logical (Design) Ontology
Or get rid of it!
Application Ontology

39. Summary
Methodology: Understanding types of models
Semiotics (what the model represents)
Formalism (how it represents it)
Purpose (Logical v Conceptual)
Truth is not meaning
Syntax is not semantics
Words play games
Ontologies
Can be conceptual or “logical” (data focused)
Can have correspondence or foundational semantics
Conceptual ontologies use upper and cross domain ontologies
Use the best of the research that is out there
Applications
Different use cases suggest that different styles of ontology are most appropriate
Most use cases focus on data in some way and so need logical ontology style
Migration – no big bang needed; evolve from data ad dictionaries to concept ontology

40. Thank You!
Mike Bennett