FIBO Semantics Initiative

David Newman

Strategic Planning Manager, Vice President

Enterprise Architecture, Wells Fargo Bank

January 2012

2

"We can't solve problems by using thesame kind of thinking we used whenwe created them." —Albert Einstein

1/30/2012

Agenda

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2) Business and Regulatory Drivers2) Business and Regulatory Drivers

3) Briefing on Semantics as an Enabling Technology for Expressing andOperationalizing Financial Data Standards

3) Briefing on Semantics as an Enabling Technology for Expressing andOperationalizing Financial Data Standards

4) OTC Derivatives POC Demonstration4) OTC Derivatives POC Demonstration

1) Mission of joint EDM Council/Object Management Group Semantics OTCDerivatives Proof of Concept

1) Mission of joint EDM Council/Object Management Group Semantics OTCDerivatives Proof of Concept

5) Discussion and Next Steps5) Discussion and Next Steps

Industry Team Collaborating onSemantics OTC Derivatives POC

Name Organization Role

David Newman Wells Fargo Lead

Mike Bennett EDM Council Core Team

Elisa Kendall Thematix Core Team

Jim Rhyne Thematix Core Team

Mike Atkin EDM Council Stakeholder

Anthony Coates Londata Subject Matter Expert

David Gertler Super Derivatives Subject Matter Expert

Marc Gratacos ISDA Subject Matter Expert

Andrew Jacobs UBS Subject Matter Expert

Dave McComb Semantic Arts Subject Matter Expert

Pete Rivett Adaptive Subject Matter Expert

Martin Sexton London Market Systems Subject Matter Expert

Harsh Sharma Citi Subject Matter Expert

Kevin Tyson JP Morgan Chase Subject Matter Expert

Marcelle von Wendland Fincore Subject Matter Expert

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Key Regulatory RequirementsInfluencing Semantics OTC POC

5

1) Define Uniform and Expressive Financial Data StandardsAbility to enable standardized terminology and uniform meaning of financial data forinteroperability across messaging protocols and data sources for data rollups and aggregations

1) Define Uniform and Expressive Financial Data StandardsAbility to enable standardized terminology and uniform meaning of financial data forinteroperability across messaging protocols and data sources for data rollups and aggregations

2) Classify Financial Instruments into Asset Classes*Ability to classify financial instruments into asset classes and taxonomies based upon thecharacteristics and attributes of the instrument itself, rather than relying on descriptive codes

2) Classify Financial Instruments into Asset Classes*Ability to classify financial instruments into asset classes and taxonomies based upon thecharacteristics and attributes of the instrument itself, rather than relying on descriptive codes

3) Electronically Express Contractual Provisions**Ability to encode concepts in machine readable form that describe key provisions specified incontracts in order to identify levels of risk and exposures

3) Electronically Express Contractual Provisions**Ability to encode concepts in machine readable form that describe key provisions specified incontracts in order to identify levels of risk and exposures

5) Meet Regulatory Requirements, Control IT Costs, Incrementally DeployAbility to define data standards, store and access data, flexibly refactor data schemas and changeassumptions without risk of incurring high IT costs and delays, evolve incrementally

5) Meet Regulatory Requirements, Control IT Costs, Incrementally DeployAbility to define data standards, store and access data, flexibly refactor data schemas and changeassumptions without risk of incurring high IT costs and delays, evolve incrementally

4) Link Disparate Information for Risk Analysis *Ability to link disparate information based upon explicit or implied relationships for risk analysisand reporting, e.g. legal entity ownership hierarchies for counter-party risk assessment

4) Link Disparate Information for Risk Analysis *Ability to link disparate information based upon explicit or implied relationships for risk analysisand reporting, e.g. legal entity ownership hierarchies for counter-party risk assessment

*Swap Data Recordkeeping and Reporting Requirements, CFTC, Dec 8, 2010*Report on OTC Derivatives Data Reporting and Aggregation Requirements, the International Organization of Securities Commissioners (IOSCO), August 2011**Joint Study on the Feasibility of Mandating Algorithmic Descriptions for Derivatives, SEC/CFTC, April 2011

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Semantics OTC Derivatives POC Mission

Mission Statement:

Demonstrate to the financial industry and the regulatory communityhow:

utilizing semantic technology and the Financial Industry Business Ontology(FIBO) can be a prudent strategic investment to realize:

data standardization

data integration

data linkage

data classification

using currently available data sources and messaging protocols

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Data challenges for entity and instrumentidentification, classification and relationships

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How can we supplement our existing investments in datamanagement to resolve these challenges and achieve these goals?

Current State of Financial Data

Limited data standards

Data rationalization problems

Data incongruity and fragmentation

Opaque data silos limits integration

Cryptic codes, programs, brittledata schemas and fixed taxonomies

Jackson Pollock “Convergence”

How can weevolve from astate of datadisorder to dataorder?

Target State of Financial Data

Pervasive data standards

Data precision, clarity, consistency

Data alignment and linkage

Data integration despite silos

Flexible and intelligent data schemasand dynamic classifications

Semantic Web Technology Can Help OrganizationsMature their Data Management Capabilities

• The true value of an information management system isultimately based upon the intelligence and expressivepower of it’s data schema or model

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• Semantic web technology provides highly advanced dataschemas (ontologies) and tools that can help organizationsbetter define, link, integrate and classify their data

Financial Industry Business Ontology (FIBO)

Industry initiative to extend financial industry data standards using semanticweb principles for heightened data expressivity, consistency, linkage and rollupsSemantics is synergistic, complementary and additive to existing data standardsand technology investments in data management!

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Built in

FIBO

Securities

Loans

BusinessEntities

CorporateActions

Derivatives

What is Semantic Technology?

10

A data management technology for the 21st century that provides:a layer of intelligence over disparate data structures that is used to precisely express the meanings, concepts, and relationshipsimplied by the data in ways that both humans and machines can understand in order to maximize data organization, integration andclassification

Semantic Web Stack1/30/2012

What are Semantic Data Schemas(Ontologies)?

• Schemas based on a formal symbolic logic (Description Logics) that

• specifies a set of mathematically verifiable and repeatable logicalpatterns that are understood by machines

• and can be used to represent complex relations between entities

• in order to automatically describe real world concepts that aremeaningful to humans

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Understands Understands

Semantic Schema (ontology)

Semantic Technology Basics

• Describes concepts in terms of:

– Classes (Entities, Unarypredicates)

– Relationships (Properties, Binarypredicates)

– Individuals (instances)

• Makes inferencing possible

– A “Reasoner” infers new datarelationships and classificationsafter applying semanticallydefined rules and logical patternsto instances of data

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David isEmployedBy Wells Fargo

Subject<<Class>>

Person

Subject<<Class>>

Person

Predicate<<Property>>

workFor

Predicate<<Property>>

workFor

Object<<Class>>

Company

Object<<Class>>

Company

Aligns linguistically with howwe think and speak!

employsinverse

subPropertyOftype type

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Semantic Intelligence Utilizes UnderlyingMachine Based Logical Patterns

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Inference: Humans cause Forest Fires

A B C D

Inference: A causes DUnderlyingMachineBasedLogicalPattern(Axiom)

HumanConcept

A causes B B causes C C causes D

Example: Transitive Relations

expresses

Use Cases : Ancestry, Dependency, Impact, Link Analysis

Some Examples of Semantic Axioms that AllowMachines to Represent Human Concepts

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Subsumption

FunctionalProperties

SymmetricProperties

TransitiveProperties

PropertyChains

RestrictionClasses

Lisa hasBirthMother Marge

Person hasBirthMother Mother

Person marriedTo Person

Bart hasAncestor Homer and Homer hasAncestor Abraham -> BarthasAncestor Abraham

Person hasParent Person: Person hasSister FemalePerson-> Person hasAunt FemalePerson

Person hasAncestor Person

Bart hasParent Marge : Marge hasSister Selma-> Bart hasAunt Selma

Properties can belinked together to forma chain of meaningfulrelationships

If A has a relation withB, and B has a relationwith C, then A also hasa relation with C

Property can have onlyone unique value

NuclearFamily equivalentClass = hasFather exactly 1 Father andhasMother exactly 1 Mother and hasChild some Child

Mother subClassOf Parent

Simpsons type NuclearFamily -> hasFather Homer and hasMotherMarge and hasChild (Bart, Lisa, Maggie)

Describes new class byassociating multipleclasses, properties and

values together

Marge type Mother -> Marge type Parent

A class (or property) isa sub-set of anotherclass (or property)

Homer marriedTo Marge -> Marge marriedTo Homer

Property relation holdstrue in both directionsof the relationship

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Ontology Spectrum*

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weak semanticsweak semantics

strong semantics

Is Disjoint Subclass ofwith transitivityproperty

Modal Logic

Logical Theory

Thesaurus Has Narrower Meaning Than

TaxonomyIs Sub-Classification of

Conceptual ModelIs Subclass of

DB Schemas, XML Schema

UML

First Order Logic

RelationalModel, XML

ER

Extended ER

Description LogicDAML+OIL, OWL

RDF/SXTM

Syntactic Interoperability

Structural Interoperability

Semantic Interoperability

From

less

tom

oreexpre

ssive

*courtesy of Dr. Leo Obrst, The Mitre Corporation*courtesy of Dr. Leo Obrst, The Mitre Corporation1/30/2012

Semantics Offers Differentiating ValueCompared to Conventional Technologies

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Swap

Swapstream

PartySwapAssoc

SwapParty

LegalEntity

XML Relational Semantics

InterestRate SwapContract

SubClassOf

Swap_100001234

Type

Swap_Leg someFixed_Interest_Rate andSwap_Leg someVariable_Interest_Rate

EquivalentClass

SwapStream_1…

hasSwapStream

SwapStream_2…

hasSwapStream

VanillaInterest Rate

SwapContract

BasisSwap

Contract

SubClassOf

Swap_LegType

• Lingua franca of web service messagingpayloads following W3C standards• Used to tag data elements with standardlabels that conform to a predefined schema• Forms structured data hierarchies• Document hierarchy can be queried

• While XML tags associate data to labels,the meaning of the labels is not inherentlyunderstood by the computer requiringcustom program logic to process each label

• Dominant databaseimplementation• Highly mature software and tools• Data is physically organized withintables and accessed by matchingrelated columns in different tablesthat fulfill various conditions• Knowledge within application logic• Hard-wired and brittle schema/data• Design, construction, access, mgtare labor, time, resource intensive

• Limited, but growing, set of software, tools• Can supplement XML and relational database• Can begin with knowledge representationand evolve towards operationalimplementations

• Emerging form of knowledge representationoffers highly intelligent form of dataorganization• Conceptually describes the meaning of dataand its relationships in a way that both peopleand computers can understand• Supports classification, reasoning and agility

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Semantics Supplements Existing DataStandards: Descriptively and Operationally

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InterestRate SwapContract

SubClassOf

Swap_100001234

Type

Swap_Leg someFixed_Interest_RateandSwap_Leg someVariable_Interest_Rate

Equivalent

Class

SwapStream_1

hasSwapStream

SwapStream_2…

hasSwapStream

VanillaInterest Rate

SwapContract

BasisSwap

Contract

SubClassOf

Swap_LegType

Ontology

XML MessageXML Message

Describes Describes

Rationalizes

Provides datamapping, linkageand classification

Operational

Precisely describesdata elements for

better humanunderstanding

Descriptive

Integrates

Operational

Provides dataintegration and

advanced queriesacross disparate

data sources

Swap

Swapstream

PartySwapAssoc

SwapParty

LegalEntity

Relational Data Base

Swap

Swapstream

PartySwapAssoc

SwapParty

LegalEntity

Relational Data Base

Note: Run with Animation

Semantic Technology: How is it beneficial?

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Knowledge encapsulated inopaque software

Data organization tightly coupledwith schema

Multiple complex tables and datarelationships

Awareness of physical organizationof data required

Schemas enforce limited dataintegrity

-> High costs, longer TTM

Conventional Technology

Semantic Technology

Standard vocabulary andknowledge representation

Data organization decoupled fromschema

Inferencing creates newknowledge

Consistent rules based on standarddata elements ensured acrossdomain

All data is Web addressable

-> Lower costs, faster TTM

Challenges:

Improvements:

Data Schema

New Data Entity

Physical Database

New Physical Table for New Entity

Application Software

Business Rules in Code

Access

Update

Define

New Data Entity

Ontology / Semantic SchemaPhysical Database

Some BusinessRules Added toOntology

ApplicationSoftware

Inferred

Some Business RulesMigratedto Ontology

Physical Format Unchanged after NewData Entity Added

Access

Update

Define

Data

Schema

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Comparative Analysis

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XML Relational Semantics

Describes Concepts, Taxonomies, Rich Data Relationships

Concepts Understandable to Both Humans and Machines

Multiple Classifications and Categorizations of Data

Logical consistency and constraint checking

Reasoning and Inference Capabilities

Ability to change schema/model with low impact/cost

Potential to Deliver Faster TTM and Lower TCO

Operational Scalability, Efficiency and Optimization

Industry Adoption and Prevalence of Skilled Resources

Maturity of Tools and Software

Current Ease of Mastery of Technology and Skills

Low Medium High1/30/2012

Potential Benefits of using SemanticTechnology

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Reduce Complexity•Reduces reliance on arcane legacy data structures and cryptic codes by using more meaningful,natural language friendly constructs

Evolve Global Data Standards, Enable Data Integration and Classification• Provides model and infrastructure to define the meaning of information in order to represent the

semantics of data standards; as well as integrate, link and classify incongruent data

Reduce Costs (People and Technology)• As understanding of data increases, costly data reconciliation efforts by analysts can be reduced• Improved data federation and reduced data management costs can potentially be realized

Improve Agility• As regulatory/industry views and assumptions change, semantics allows data schemas to rapidly

reflect change without incurring massive data and application program restructuring efforts

Increase Functionality using Reasoning and Inferencing Capabilities• Using logically consistent rules and semantic definitions, programs called reasoners can infer data

to be classified into special business defined categories and relationships

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Business and Operational Ontologies

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Defines Transaction types

Defines contract types

Defines leg roles Defines contract terms

Operational Ontology(Semantic Web)

IR Stream

IR Stream

IRSwap

Agreement

has party

has party

is a

swaps

swaps

Includes only those termswhich have correspondinginstance data

Requirement #1: Define Uniform and Expressive Financial Data Standards

Model from SparxSystemsEnterpriseArchitect

Business Ontology(AKA “conceptualmodel”)

provides source for

Narrowed forOperational use

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Anatomy of a Semantic Data Standard

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RDF

Type

OWL

versionInfo

SemanticMetadataModel

SKOS

definitionDC

source

ODM Model

RDFS

seeAlso

SKOS

altLabel

RDF,RDFS, OWL: W3C Semantic languagesDC: Dublin Core Metadata ElementsSKOS: Simple Knowledge OrganizationSystem

rdf:type LegalEntityIdentifierskos:altLabel LEIskos:definition A legal entity identifier (LEI) is a unique ID

associated with a single corporate entitydc:source SIFMA (Securities Industry and Financial

Markets Association) overview discussion ofLegal Entity Identifier (http://www.sifma.org)

owl:versionInfo Version 1.0.0rdfs:seeAlso Office of Financial Research; Statement on

Legal Entity Identification for FinancialContracts

SKOS

altLabel

Semantic Metadata

Multiple access options over the web via the authoritativestandards body Hyperlink to semantic web standard from documents Community participation and interaction Query access via formal semantics repository including linksand synonymous terms for knowledge Improved governance Provenance and evolution recordedModel files for download in multiple tools

Community Access to Standards

Requirement #1: Define Uniform and Expressive Financial Data Standards

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Semantics can operationally classifyundifferentiated Swaps and show relationships

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Classes are inferredusing rules that query

the content of the data

Data is linked togethervia relationships called

properties

* Gruff 3.0 courtesy of Franz, Inc.

Vanilla_IR_Swaphas_Swap_Legs someVariable_Interest_Termsand has_Swap_Legs someFixed_Interest_Terms

Requirement #2: Classify Financial Instruments into Asset Classes

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Semantic Representation of ContractualProvisions for Risk Classification

24

Requirement #3: Electronically Express Contractual Provisions

Note: OTC POC Phase 2 in process

Define Axioms

Identify KeyContractual

Events

Identify KeyContractual

Actions

ISDA Master Agreement Schedules Credit Support Annex Schedules

DowngradeCounterparty

Credit

CreditRatingAgency

Default Events

TerminationEvents

Increase Collateral

Transfer Payments

ClassifyCounterparties intoRisk Categories for

Analytics

Reduce Valueof

Collateral

Events

Counterparties

OTC Derivative Confirm

ClassifyContract

Type

InferCounterparty

Exposures

Risk Analyst

TransactionRepository, et.al.

*Report on OTC Derivatives Data Reporting and Aggregation Requirements, theInternational Organization of Securities Commissioners (IOSCO), August 2011

**Joint Study on the Feasibility of Mandating Algorithmic Descriptions for Derivatives,SEC/CFTC, April 2011

Market ReferenceData

FpML

FIBOOntology

OperationalOntology

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?entity

LegalEntitytype

?legalName

hasExactLegalName

?parent

hasImmediateParent

?swap

partyToSwap

?amount

notionalAmountAtRisk

Transaction Repository Z

Semantics offers Advanced Query Capabilities

25

Requirement #4: Link Disparate Information for Risk Analysis

?entity

LegalEntitytype

?legalName

hasExactLegalName

?parent

hasImmediateParent

?swappartyToSw

ap

?amount

notionalAmountAtRisk

Transaction Repository Y

Data is queried using graph pattern matching techniques vs. relational joins Queries can process inferred data and highly complex and abstract data structures Queries can federate across semantic endpoints (using SPARQL 1.1) Data can be aggregated and summarized (using SPARQL 1.1)

Risk Analyst

?entity

LegalEntitytype

?legalName

hasExactLegalName

?parent

hasImmediateParent

?swap

partyToSwap

?amount

swapNotionalAmount

Transaction Repository X

Query all Transaction Repositories toreport on the sum total of aggregateexposure for all counterparties andtheir parents involved in all swapsassociated with an interest rateswap taxonomy

Note: TBD in future phase of POC

InterestRate Swap

BasisSwap

type type Vanilla InterestRate Swap

subClassOf

subClassOf

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Semantics Offers Federation via Linked Data

26

Requirement #4: Link Disparate Information for Risk Analysis

Semantically defined data that is Web addressable and “inter-linked”

Transcends organizational boundaries and provides universal access to data wherever it residesinternally within the network (and externally via “Linked Open Data”)

Obtains data directly from its source (transparent to location, platform, schema, format)

Can support access, queries and rollups across Swap Data Repositories

Semantic EnterpriseInformation

Integration (EII)Platform

Swap Data RepositoryDatabase

Note: TBD in future phase of POC

Ontologies

Links to the

Semantic Web

Linked OTC Data Cloud

Legal EntityData Provider

Risk Analyst

Swap Data RepositoryDatabase

AggregatedLinked DataQuery

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Data

XML

RelationalSemantic

Application

Unstructured

SchemaXML

Relational

Application

BusinessSemantics

ConceptualModels

Business Semantics Conceptual Models• Primarily for Human consumption• Conceptual, design-time, and non-operational• Community engagement and update process when warranted• Standard terminology, concepts and descriptions for reference,knowledge, data reconciliation, rationalization and governance• Integrated ontologies, Upper ontologies for broader meaning

Semantic Usage Patterns can be DeployedIncrementally and in Tandem with Existing

Technology

27

Reference Ontologies• Primarily for Machine consumption• Ontologies narrowed for operational usage• Supplements and operates in tandem with conventional technology• Runtime access to knowledge, reference data, metadata• Canonical domain models for mappings and interoperability• Semantic graph pattern matching queries and automated reasoning

Data

Inferred

Schema

Inferencing and Classification of Source Data• Heterogeneous source data ingested, validated forinconsistencies, and transformed by Semantic Reasoner intodomain ontology to fulfill mapping rules• Source data inferred by Reasoner, using formal axioms or rules,into abstract classifications, new data relationships/linkages• Semantic rules engine can be optionally accessed• Query time reasoning can be optionally utilized

ABox

Inferred

TBox

ABox

Inferred

TBox

Data

Inferred

Schema

Data Federation and Linked Open Data• Data semantically linked, integrated and accessed both internallyand externally using RDF linked URIs which are Web addressable• Federated query of semantic and non-semantic data stores usingcanonical semantic domain model for data interoperability andinferencing

SemanticApplication

SemanticApplication

XML

Unstructured

RDBMSRelational

Unstructured

XMLXML

Unstructured

RDBMSRelational

Unstructured

XML

Rules EngineRules Engine

Conceptual Ontology Operational Ontology

Operational Ontology Operational Ontology

Requirement #5: Meet Regulatory Requirements, Control IT Costs, Deploy Incrementally

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FpMLSwap

FpMLSwap

OTC POC Semantic Building Blocks andMethodology

28

FIBOSwap

FIBOSwap

FIBO-FpMLSwap Bridge

LegalEntityFIBOSwapBridge

FpML InstancesSPARQL Queries

2) Build operationalontology for Swapsfrom FIBO

1) Build conceptualontology for Swapsin FIBO

3) Build operationalontology for Swapsfrom FpML

4) Build operationalontology for Legal Entities

5) Build bridging ontologiesthat tie together individualontologies

6) Ingest FpML Swap datainto FpML Swap ontology

8) Invoke Reasoner toa. associate data in FpML

Swap Ontology to FIBOSwap Ontology

b. classify Swap Contractsinto taxonomy levelsaccording to theirattributes

9) Perform queries to fulfillregulatory use cases and reports

OTC POC Operational Ontologies

LegalEntityFpMLSwapBridge

Legal Entity

KnowledgeFIBO

Model

UpperOntologies

LE Instances

7) Ingest Legal Entity datainto Legal Entity Ontology

Reasoning

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OTC Derivatives SemanticPOC Demonstration

• Swap Ontology

• Classification and Reasoning

• Semantic Query

291/30/2012

Semantic Building Blocks for Financial DataStandards and Risk Management

Semantic Descriptions of Financial Data,Concepts, Relationships and Rules

Higher Level Concepts (Upper Ontologies)

Mortgages Securities Derivatives

Hu

man

Facing

Mach

ine

Facing

De

scriptive

Semantic Foundations for Financial Data Management

Op

eratio

nal

Data Consistency

Data Traceability

Data Taxonomies

Data Mapping and Integration

Data Classification and Categorization

Inferred Conclusions and Data Linkage

Graph Pattern Matching

Data Federation

Data Rollups and Aggregation

Transp

are

ncy

Asset andRisk

Categories

SystemicRisk

Analysis

Trust

Co

nce

ptu

alO

nto

logie

s

Data andKnowledge

Representation

Reasoning andInferencing

AdvancedQueries

FinancialData

Standards

Holistic Data Linkages and Bridges

RuntimeKnowledge

Expre

ssivity

Imp

lem

en

tation

On

tolo

gies

...

FinancialIndustryBusinessOntology

(FIBO)

1/30/201230

Adoption can be anEvolutionary

Process that mayLead to Strategic

Value

• Is still early in its lifecycle; tools are relatively immature andlanguage standards are still evolving, vendors are small

• Does require a learning curve to understand how the “semanticreasoner” thinks in order to best utilize the technology; which cantake time and investment to develop

• Will not necessarily replace current object oriented and relationaldatabase technology in the foreseeable future; but can be used tobetter enable and enhance conventional technology

• Positions users that are adopters of its knowledge representationand reasoning capabilities to achieve valuable benefits not easilyachievable using conventional technologies by themselves

Semantic Technology:

31

Making the Investment in Semantic Technology

By embracing semantic technology and FIBO as a basisfor enhancing financial industry data standards we aremaking a strategic investment to improve our datamanagement capabilities by using the tools of the 21st

century

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Invitation to Financial Regulators, MarketAuthorities and the Financial Industry

32

Financial regulators to support and participate in a formal collaboration with financialindustry participants and standards organizations such as ISDA, ISO, XBRL, FIX, MISMO,OMG, etc. to refine and implement FIBO as the standard financial instrument and entityontology for regulatory reporting, business processing and risk analysis

Financial regulators to act as catalysts in forming a public/private partnership to createbest practice reference architectures for operational semantic implementations.

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FIBOFIBO

Continued extension of the semantic proof-of-concept work to support the analyticalrequirements of regulators, market authorities and financial institutionsOTC Derivatives (Contractual Provisions, Credit Default Swaps)Asset Backed Securities (Mortgage Backed Securities, Collateralized Debt Obligations)