Transcript Slide 1

The Financial Industry Business
Ontology
Explanatory Material
Mike Bennett, EDM Council
July 24 2012
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Overview
• Definition of an ontology
• Overview of classification theory
• Transformation from a taxonomy to an
ontology.
• The Financial Industry Business Ontology
(FIBO)
• From business semantics to an operational
ontology
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Data Governance
• A Bank is in essence an IT Company
– Software manufacturing
– Data production, consumption,
– Information supply chain
• So how do we manage the business
view of data?
– Language interface business to IT
– Conceptual model
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Managing Semantics
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Conceptual Model for Data
Conceptual Model (Semantics)
Realise
Logical Model (Design)
Implement
Physical Model (Implementation specific)
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Conceptual Model for Data
Business
Conceptual Model (Semantics)
The Language
Interface
Logical Model (Design)
Technology
Physical Model (Implementation specific)
FIBO bridges the “Language gap” between business and
technology
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Development Lifecycle for Data
Level (from Zachman)
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Data
Function
0
Scope
(contextual)
Things relevant to the
business
Set of business processes
1
Business Model
(conceptual)
Semantic Model
Functional Requirements
(Use Case)
2
System Model
(logical)
Logical Data Model
Logical Design
3
Technology Model
(physical)
Physical Data Model
Physical Design
4
Detailed Representation
Data definition
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Development Lifecycle for Data
Level (from Zachman)
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Data
Function
0
Scope
(contextual)
Things relevant to the
business
Set of business processes
1
Business Model
(conceptual)
Semantic Model
Functional Requirements
(Use Case)
2
System Model
(logical)
Logical Data Model
Logical Design
3
Technology Model
(physical)
Physical Data Model
Physical Design
4
Detailed Representation
Data definition
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Conceptual Model Requirements
• Must be owned and validated by
business
– Manage the “Language interface” between
tech and business subject matter experts
– Everything should be in English
• No techie terms and casing like
“objectProperty”
– Everything should be reviewable
• Spreadsheets
• dialect-free diagrams
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Industry Conclusions
• Good design is weak semantics
• Business knowledge gained during reviews is either
– Lost
– Buried in meeting minutes
– Kept in uncontrolled spreadsheets in a variety of structures
• Data Dictionaries try to link business definitions to
data elements
– but data elements are reused across business meanings
and usage contexts (good design again)
• Industry conclusion
– “We need a semantics standard”
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Ontology
• “A formal specification of a
conceptualization”
• But
– What formalization?
– What conceptualization?
– That defines what sort of ontology
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Some Terms
• Taxonomy
– A structured classification scheme
• Linnaeus Taxonomy of Species
• Taxonomy of Financial Instruments
• Ontology
– Adds formal properties to a taxonomy
– Describes real world things
• Vocabulary or Lexicon
– Deals with the words for things
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Overview of Classification Theory
• “Classification”
– a system that employs a “meaningful
clustering” of items
• Kwashnik (1999)
– the “orderly and systematic arrangement”
of items into a “system of mutually
exclusive and nonoverlapping classes”
• Jacob (2004)
• There are various kinds of classification
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Classification – General View
• A Classification is a hierarchical structure
• This has two properties (Loehrlein 2012)
– a hierarchical structure organizes categories on
some sort of continuum.
– could be "big to small," "general to specific," "powerful to
not powerful," etc.
– more categories occupy one end of the continuum
than the other
• One such hierarchy is a type hierarchy
– That is, a classification of some things, in some
domain of discourse, from the general to the
specific
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Classification Requirements
• Classification schemes may be
– Monohierarchical
– Polyhierarchical
• Polyhierarchical classification depends on
multiple inheritance
– one class may have several parents
• A whale is both a marine animal and a mammal
• An IR Swap is both a Swap Contract and an Interest
Rate Derivative
• There is no one right way to classify
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Type Hierarchy Classification
• A given set of sub-classes of some class:
– Should be divided according to one organizing principle
– That is, one fact about the class of thing, which varies in a
particular way
– Put simply: one property which differs for each
• Classification facets may or may not be
– Mutually exclusive: each set excludes the others
– Completely exhaustive: all sub-classes between them cover
the full membership of the superclass
• Realistically, one class of thing may be divided in
many such ways
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Example Classification Scheme Types
• Non Hierarchical
– Unstructured list of categories
• Things not connected by relationships
– Pyramid structures
• Things connected systematically by a relationship
that is not the generic relationship
– Military hierarchy (a soldier is not a kind of general)
– Geographic (Ontario is not a type of Canada)
• Strict hierarchy: a thing may only be under one
category
• Polyhierarchy: supports multiple category
membership
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Type Classification Hierarchies
• Precise inclusion sets v imprecise inclusion
sets
• Well established species v ad hoc species
• Topic hierarchies
– E.g. Dewey Decimal: French Grammar is not a
kind of French, but a book on French Grammar is
a kind of book on French
• Faceted schemes
– Use multiple type hierarchies
– May arrange this in different priority orders, for
different purposes
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Taxonomy
• Taxonomy:
– system that can be used to group, arrange, and
describe items according to meaningful principles,
and which provides users with an overview of the
domain being organized
• Lambe (2009)
• A taxonomy uses a classification scheme to
arrange the items in the domain of discourse
• A Taxonomy forms the basis for any ontology
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From Taxonomy to Ontology
• Ontology: the study of what is
• Ontologies (plural): the real world
universe as it is referred to in a
computer application
– Informal: every application has an
ontology, whether it’s documented or not
– Formal: uses formal logic in some notation
• Semantic Web
– Uses a formalism which can be reasoned
over
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Model Theory and Semiotics
• For any model we may ask:
– What is that to which the model elements
correspond?
– What is the formal grounding of the
symbols in the model
• For an ontology:
– The things to which the model elements
refer are real things in the domain of
discourse
– The grounding is formal logic
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Possible classes of Thing
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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".
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What is an Equity?
Or to put it another way…
Financial
Instrument
Equity
Is a kind of
Equity
security
In relation to
Instrument
Terms
Has rights defined in
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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
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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
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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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Making it Meaningful
• Putting something into RDF/OWL does not
make it meaningful
– Only you can do that
• So, what is a meaningful model
– 1. Formal relationship between model and subject
matter:
• “Everything is a Thing”
– 2. Formal notation grounded in common logic
– 3. Abstraction of kinds of thing into their simplest
possible building blocks
• Contracts, Parties, Legal Entities etc.
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Making it Meaningful
• Formal Logic
• Semiotics – what to the model elements
stand for
– Ontological commitment
• Symbol grounding – what are the model
elements in logical terms
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Formal Logic
• Ontology models rely on two logic constructs
from formal logic
– Universal Quantifier
– Existential Qualifier
: “For all”
: “There exists”
• These make up the “First Order Logic”
• Allows you to define Things and Facts
– Things: sets of which something may be a
member
– Facts: properties which intensionally define
membership of that set
– Can also describe sets extensionally
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Formal Logic
• Lets us assert the existence of things
• Lets us state, for given things, facts
about them
– These are properties
– How it looks:
• You would not want to present these to
business subject matter experts!
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Theory of Meaning – in English
• The model consists of:
– Things
• A Thing is a set theory construct
• Arranged in a hierarchy called a “Taxonomy”
– Like taxonomy of species
– Facts
• Simple facts (names, dates etc.)
– e.g. “Issue Date” is a date
• Relationship Facts (relate one thing to another thing)
– e.g. “Share confers Voting Rights”
– Things so referenced are also in taxonomic hierarchies
– Other set theory concepts
• Disjoints, Unions
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Theory of Meaning – in English
• Taxonomy: Like Taxonomy of Species
– Animal v Plant
– Vertebrate v invertebrate
– Mammals, fish etc.
• Each thing is defined by what facts distinguish it
• For each new thing:
– What sort of thing is it?
– What facts distinguish it from other things?
• If it walks like a duck, swims like a duck and quacks
like a duck, it belongs to the set of all things that are
a duck
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Applying Meaning to Financial Semantics
• Everything is a Thing
– What kind of Thing?
– What distinguishes it from other things?
• What kind of Thing?
– Share is a Security is a Transferable Contract … is
a Contract
• What properties?
– Share gives the holder some Equity
– Share confers on the holder some Voting Rights
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Where does this lead?
• Taxonomy of kinds of contract
• Taxonomy of kinds of Rights
– Rights, Obligations are similar and reciprocal
concepts
– Note that these don’t necessarily correspond to
data
• Semantics of accounting concepts
– Equity, Debt in relation to assets, liabilities
– Cashflows etc.
• Semantics of countries, math, legal etc.
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Global Terms
• Rationale:
– Everything is a specialization of some more general term
• Legal, accounting, events, transaction semantics
– Facts about instruments are stated in terms of other things
• Countries, formulae etc.
• Want to derive from and align with the best ontologies for these
area
• Disposed under a common framework
• FIBO models are extensively partitioned
• Shared Semantics:
– Align with standard ontologies where these exist
– Leverage OMG standards e.g. Date Time Vocabulary
• Work with academia and standards (ongoing)
– Transaction Semantics: REA, XBRL-GL
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Financial Industry Business Ontology
User Commitments
Original
XBRL Content
Boxes & Lines
SME Reviews
Industry
Standards
XLS
ISO 20022
FpML
MDDL
FIBO
Archetypes
Theory of meaning
UML
Tools
Sub-set for
readability
SemWeb OWL
constructs
RDF/OWL
ODM
ODM v1.1
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What we wanted
• Business meanings
• In business language
• For business people
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What we wanted
• Business meanings
– Not data dictionary
• In business language
– Not a design
• For business people
– No funny symbols and things
– No language to learn
– Just the facts
– Boxes and lines – something like this…
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Sample Screenshot
Thing
“Is A”
relations
Relationship Fact
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Example: Credit Default Swap (CDS)
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Spreadsheet
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Sample screenshot 2:
Different types of Thing
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So what is FIBO
• FIBO has these distinct aspects:
– The Business Ontology
– Presentation for Business Readability
• Released in discrete ontologies by subject area
– FIBO for Business Entities is currently under
submission
– Securities, Loans, Derivatives to follow
– Corporate Actions, Transactions later
• Leverage other OMG standards and shared semantics
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From Business to Operational Ontology
• Uses for FIBO
• Semantic Technology applications
• Conceptual versus Operational
Ontologies
• Transforming from one to the other
• Use of metadata
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FIBO Uses
Conventional Tech
MDR
Repository
Semantic Web
FIBO
XLS
OWL
Model
Semantic Data Model
Reasoners
Logical Data Model
Mapping
Linked Data
Semantic Query
Physical Data Model
Model Driven Development
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FIBO Uses
• As a common reference point
– Mapping, integration
– Replaces ad hoc spreadsheets with a formal project
deliverable
– Extend locally for concepts within the firm
• Model Driven Development
– Position as “Business conceptual model”
– Manage the “language interface” between Business
and IT
• Semantic Technology applications
– Implemented across conventional data stores
– New application infrastructures (Triple stores)
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FIBO Semantic Technology Applications
• Model one get one free
– Full and formal representation of the business
facts as a common language across the enterprise
– Rendition of this in Semantic Web format (OWL)
opens the way to semantic technology
applications
• Formal reasoning across subject matter
• Automatic classification of product types
• Querying across subject matter
• Business Conceptual ontology (FIBO)
transformed into “Operational Ontology”
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Conceptual and Operational Ontology
• Conceptual Ontology
– Includes concepts like rights, obligations
• Meaning is grounded in law
– Does not care if it is decidable or how long it takes to reason
over it
• Operational Ontology
– Must conform with the stated technical constraints
• Reasoning
• Decidability
– Combines
• ontology (classes) with
• “individuals” (instance data in triple store format)
• How to get from one to the other?
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How to get from one to the other
• Select a single classification facet
• Collapse the taxonomy above the domain
• Ignore terms which do not correspond to data
– Rights and obligations
– Policies, strategies, goals
• Identify those terms which correspond to instance
data
– For most rights and obligations, some data signature is likely
to be present
• Use property chaining in the conceptual ontology to
relate several more abstract but meaningful
properties, with one concrete and data-focused
property which can be processed.
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Ontology Metadata
• Standard metadata for definitions,
notes, provenance etc.
• Additional metadata for mapping,
regulatory cross reference etc.
• Available in OWL versions of FIBO
– Annotation Properties: not reasoned over
– Object Properties: seen by reasoner
– Both are visible to semantic querying
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FIBO Roadmap (as at July 2012)
June
Sept
Dec
Q3, 2012
Mar
Q4, 2012
June
Q1, 2013
Q2, 2013
Q3, 2013
Industry feedback
Finalization TF
FIBO-Foundations
Vote
FIBO-Foundations
Global Terms and modeling framework
Vote
FIBO-BE
Industry feedback
Business Entity
DTV Alignment
I
Operational Ontology
Task Force
Updates
FIBO Reference Data
Securities
Operational
Ontology
FIBO Reference
Data
Derivatives Part 1
Vote
Industry feedback
FIBO-BE
Finalization TF
Finalization TF
Updates
FIBO Reference Data
Updates
Vote
Industry feedback
Finalization TF
Operational
Ontology
Finalization TF
FIBO Reference
Data
Vote
Industry feedback
Derivatives Part 2
Vote
MISMO Alignment
FIBO LOANS
Funds BE
Terms
OMG DTV Alignment II
Process
notation
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Txn SME reviews
FIBO Process
Transactions
Industry feedback
FIBO Reference
Data
CIV/Funds
FIBO Date
Dependent
Market Data Ontology
FIBO Process
Corporate Actions
Finalization TF
Vote
Industry feedback
Vote
Industry feedback
Vote
Industry feedback
Vote
Industry feedback
Finalization TF
Deliverables
Adaptive: Webaccessible FIBO
presentation
FIBO Basic Business
Ontology (BBO)
FIBO OMG Specifications
FIBO Foundations
FIBO for Business Entities
Etc.
Operational Ontology
(main business use case – common
reference and querying across multiple
data sources)
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Operationa
l Ontology
Operation
al
Ontology
Main Take-away Points
• An ontology extends a taxonomy which is organized according
to some classification principles
• An ontology is not another sort of data model
– It does not replace or displace messaging standards,
database schemes or anything else
– Common semantics is about the business view of what’s in
data
– Enables mature approach to technology management
• Putting it in a SemWeb tool doesn’t make it meaningful
– You do
• Two ways to leverage FIBO
– Common semantics
– Semantic Technology applications
• Regulators and the industry are paying attention!
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Contact
• Mike Bennett
– [email protected]
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