Data Model vs Ontology Development – a FIBO perspective

1. The FIBO Journey:
To Ontology and Beyond!
Connected Data London
Savoy Place, London
16 November 2017
Mike Bennett
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2. Outline
• FIBO Motivations
• Early Explorations
• Developing a Concept Ontology
• The OWL Experience
• Refining he principles for Concept Modeling
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3. FIBO Motivations
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4. FIBO Motivations
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5. FIBO Motivations
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Common ontology
Shared business meanings

6. FIBO Motivations
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Common ontology
Shared business meanings
Validated by business
Expressed logically

7. Early Explorations
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8. 8
Possible classes of Thing

9. 9
Example “Thing”: Equity
• Real world definition of Equity:
"An equity is a financial instrument setting out a number
of terms which define rights and benefits to the holder
in relation to their holding a portion of the equity within
the issuing company".

10. 10
What is an Equity?
Or to put it another way…
Equity
Equity
security
Instrument
Terms
Financial
Instrument
Is a kind of
Has rights defined in
In relation to

11. 11
What is an Equity?
Using OWL to
define the classes
of real things in the
world, and the
facts about those
things
Modeled in
TopBraid Composer

12. 12
Financial Semantics in OWL
• Pizza approach
• “Everything is a Thing”
• What about common terms?
• accounting terms for equity, debt,
cashflow
• Places, time concepts
• Legal terms (securities are contracts)
• Better partitioning needed

13. The Semantic Web
• Web Ontology Language
• Based on Subject-Verb-Object “Triples”
• Widely used
• Protégé tool
• Experiment: Ingest a logical data model into OWL
• Result: a logical data model in OWL
• Syntax is not semantics!
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14. Developing a Concept Ontology
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15. The FIBO Moment
• Previous standardization efforts at message and data levels
• Arguments over terms
• Atkin: “What if we considered the concepts without worrying about
the words people use?”
• Sudden outbreak of peace!
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16. Financial Industry Business Ontology
Semantics Repository
Industry
Standards XLS
Boxes & LinesUser Commitments
Original
Content
ISO 20022
FpML
XBRL
SemWeb OWL
constructs ODM
EA UML
Tool
MDDL
enhancements
for readability
Theory of meaning
creates
SME Reviews
Tweaks for
Tool support
RDF/OWL
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17. FIBO: Scope and Content
Upper Ontology
FIBO Foundations: High level abstractions
FIBO Contract Ontologies
FIBO Pricing and Analytics (time-sensitive concepts)
Pricing, Yields, Analytics per instrument class
Future FIBO: Portfolios, Positions etc.
Concepts relating to individual institutions, reporting requirements etc.
FIBO Process
Corporate Actions, Securities Issuance and Securitization
Derivatives Loans, Mortgage Loans
Funds Rights and Warrants
FIBO Indices and Indicators
Securities (Common, Equities) Securities (Debt)
FIBO Business Entities
FIBO Financial Business and
Commerce
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18. The OWL Experience
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19. Two Ontological Traditions:
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Semantic WebApplied
Ontology
FIBO
 The science of
meaning
 Meaning expressed
in formal logic
 Presented in the
“Language of the
business”
 Formally grounded in
legal, accounting etc.
abstractions
 Use case specific
ontologies
 Richer internal logic
 Focus on data
 Optimized for
operational functions
(reasoning; queries)
 Addition of rules
 Mapping to other
OWL ontologies

20. FIBO Development & Feedback Ecosystem
FIBO CORE: RDF / OWL is the system of record for FIBO (everything needed for inference processing)
FIBO Vocabulary: The FIBO business conceptual model expressed in SKOS (everything needed for the unification of data across repositories)
FIBO OMG: Standards partner with EDMC for visualizing FIBO in UML (everything needed for expressing FIBO as diagrams)
FIBO.Schema.org FIBO aligned to the Schema.org community financial data used for mapping existing web pages to FIBO
FIBO CORE
RDF / OWL
FIBO Vocabulary
SKOS – RDF/S
FIBO OMG
FIBO.Schema.org
UML / SIMF
Generated
Generated
Aligned
Industry Feedback
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21. Semantic Web Applications
Swap1001
Leg 1 Leg 2
10000000
notional notional
LIBOR 3.5%
Fixed Float IR Swap
LEI5001
LEI7777
Trader LLCAcme Inc
identifies
identifies
USD
currency
Interest Rate Swap
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10000000
USD
currency
Swap
FloatingRateLeg
Inferred
Leg1 is inferred to be a
FloatingRateLeg because
any leg tied to an index is
semantically defined as
floating
Inferred
FixedRateLeg
Inferred
Leg2 is inferred to be a
FixedRateLeg because any
leg tied to an interest rate
is semantically defined as
fixed
LEI
LEI
Business EntityBusiness Entity
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
Inferred
Data for an undefined Swap
Contract before semantic
reasoning performs
classification and identification
type type
type
type
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)
Semantic reasoning
Semantic reasoning
Semantic reasoning1 2
3
isTradingWith
isTradingWith is a new
property relationship that is
inferred based on a semantic
rule and can be queried
Semantic reasoning4
fixedRateindex
• Semantic Operational Processing Reasons over Data to Infer
Classifications and Relationships
David Newman, Wells Fargo

22. Properties with No Domain or Range
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23. Two Approaches to Meaning
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Rosetta Stone Mayan Language
• Existence of already-understood
terms enabled translation
• Semantics grounded in existing
sources
• No existing common language to
enable translation
• Translation was possible only from
internal consistency of concepts

24. Internal Correspondence Semantics
• Graph has logical relations between elements
• These correspond to the relations between things in reality
• Automated reasoning checks the “deductive closure” of the
graph for consistency and completeness 24
Mayan Language

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• Directed Graph
• The meaning at each node is a product of its
connections to other nodes
• Semantically grounded at certain points in the graph
Semantic Networks
Foundational Semantics
Rosetta Stone

26. Which is Which?
• Foundational semantics:
• External grounding of concepts based on things outside the ontology
• Typically social constructs, commitments, legal and other primitives
• Each is the “simplest kind of thing” of that type
• Internal Correspondence Semantics
• The deductive closure of the whole graph is where meaning comes in
• Logic in the graph corresponds to relationships among things in the world
• Use Foundational Semantics for a business concept ontology
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27. Using OWL
Business Conceptual
Ontology (CIM)
Operational Ontology
(PSM)
Extract and Optimise
The Language Interface
Business
Technology
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28. Using OWL: Datatypes
Business Conceptual
Ontology (CIM)
Operational Ontology
(PSM)
Extract and Optimise
The Language Interface
Business
Technology
Data
types
Data
types
Platform specific matter
• So what’s that about?
• An OWL based conceptual ontology plus data seems to be a physical design artifact
• But it is still conceptual in that it represents business concepts
• Provided those concepts are expressed in data
• There are real things, the definitions of which are not based on data!28

29. Refining the principles for Concept Modeling
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30. The Three Elements
Ontology
Lexicon Data
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31. Dimensions of a Model
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Formalism
Application
Model Theoretic Relation
(grounding)
MODEL
e.g. First Order Logic
e.g. Business domain
(business process etc.)
e.g. Messaging Level

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Development Lifecycle for Data
Level
(from Zachman)
Data Function
0 Scope
(contextual)
Things relevant
to the business
Set of business
processes
1 Business Model
(conceptual)
Semantic Model Business Process
Model
2 System Model
(logical)
Logical Data
Model
Logical Design
3 Technology Model
(physical)
Physical Data
Model
Physical Design
4 Detailed
Representation
Data definition Program

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Development Lifecycle for Data
Level
(from Zachman)
Data Function
0 Scope
(contextual)
Things relevant
to the business
Set of business
processes
1 Business Model
(conceptual)
Semantic Model Business Process
Model
2 System Model
(logical)
Logical Data
Model
Logical Design
3 Technology Model
(physical)
Physical Data
Model
Physical Design
4 Detailed
Representation
Data definition Program

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This is not a more abstract
model of the solution…
Conceptual Ontology
Logical Data Model (PIM)
Physical Data Model (PSM)
Realise
Implement
The Language Interface
Business
Technology

35. 35
This is not a more abstract
model of the solution…
Conceptual Ontology
Logical Data Model (PIM)
Physical Data Model (PSM)
Realise
Implement
The Language Interface
Business
Technology
It’s a concrete model
of the problem!

36. The Semiotic Triangle (Peirce)
Concepts
Signs Real World
Objects
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37. Concepts
Diagram: Jim Odell 37

38. Semiotic Rhombus
Extensions
Signs Real World
Objects
Intensions
Concepts
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• Separate intension and
extension
• Extension can happen one,
many or no times
• The ontology is the
intensional model of
meaning
• Matters of ontological
commitment to things in
the world are based on
usage of the ontology

39. Things Information
Type A set specification for a kind of
Independent Thing that
generalizes all towers (e.g., “a tall
narrow structure”)
A set specification for a kind of
Dependent Continuant that is a
record structure containing tower
observations (e.g., a “TOWER” table
or a “#Tower” class)
Sets One of many sets of independent
things that generalize all towers
One of many sets of dependent
continuant record structures
containing tower observations (e.g.,
in that database there)
Member A member of zero or more sets of
all towers (E.g., the actual one we
call the “Eiffel Tower”)
A member of one or more sets of
record structures containing tower
observations (E.g., one that
represents the actual Eiffel Tower)
“#tower123”Represents
Introducing the Data Dimension
Jim Logan, NoMagic
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40. Data Delta
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Things the data
is about
Data-focused
Extensions
about
represents
ẟD

41. Data Delta: ẟ => 0
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Things the data
is about
about
ẟD => 0

42. Examples: Logical (data-friendly) Intensions
• Meaning of Bank: framed in terms of legal capabilities and rights
• Logical intension: presence of banking license?
• Ownership and Control
• Confer certain rights and involve certain capabilities
• These are social constructs not data
• In general: Data surrogate for real thing
• Look for signatures in data that imply the presence of real world, identifying matter
• Frame the necessary conditions for membership of a class (in a logical ontology) in
terms of what would be found (true) in data when the class of thing is there
• Inference as distinct from meaning in the original sense
• From the data you can infer that a thing exists in reality
• Real meaning – by definition mostly does not rely on data!
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43. Summary
• FIBO is available at https://spec.edmcouncil.org/fibo
• Production: Optimied for Semantic Web / Reasoning
• Development: Extensive industry content, optimized for common semantics
• Users should apply model theoretic thinking in making use of these
ontologies
• Inference processing: use optimized ontologies
• Mapping, integration, NLP: use foundational semantics
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