Semantics in Financial Services -David Newman

Semantic Applications for Financial Services: Presentation to the Silicon Valley Semantic Technology Group David Newman Strategic Planning Manager Enterprise Technology Architecture and Planning Wells Fargo Bank January 14, 2010

Agenda
The Case for Semantic Technology
Key Enterprise Business and IT Drivers for Semantic Technology
Limitations of Conventional Integration and Database Technologies
Benefits of Semantic Technology
Overview of Semantic Technology
Origins, Ontology Model, Basic Principles, Languages, Basic Concepts
Semantic Technology Providers and Adopters
Semantic Applications for Financial Services
Use Cases: Business and Technology Perspectives
Implications for Enterprise Architecture and Data Management Organizations
Recommended Semantic Technology Books and Articles
Disclaimer: The content in this presentation represents only the views of the presenter and does not represent or imply acknowledged adoption by Wells Fargo Bank

The Case for Semantic Technology

Key Business and IT Drivers for Semantic Technology
Problem:
Enterprise Data Fragmentation as a result of:
incompatible data meanings, definitions, vocabulary.
multiple incompatible physical data formats and structures
proliferation of unstructured data
multiple heterogeneous data stores across multiple siloed organizations with redundant data
Impact:
Results in less than optimum information/knowledge quality
Dilutes effectiveness and business value of data
Data integration is costly and difficult to achieve
Negatively impacts enterprise bottom line and increases risk
Goal:
Reintegrate the fragmented meanings and instances of data

Limitations of Conventional Integration and Database Technologies
Knowledge is encapsulated in opaque software
Challenge to normalize disparate data from multiple sources
Often represented in proprietary software and programs
Hard to access, should be an institutional asset
Conventional Technology Data Definition and Access Patterns Data Schema New Data Entity Physical Database New Physical Table for New Entity Application Software Business Rules in Code Access Update Define

Limitations of Conventional Integration and Database Technologies (continued)
Data schemas reflect limited knowledge
conceptual model or framework used to describe a pattern or a set of data structures
segregation between the schematic structure of the data and programmatic logic or rules that are invoked at runtime to classify data
limited to data structures and data constraints, but not to richer categorizations and rules
Data organization is tightly coupled with the schema
physical representation of the data, is dependent upon the content of the schema that defines the data
change to the schema often requires, or results in, change to the physical representation of the data

Limitations of Conventional Integration and Database Technologies (c0ntinued)
Schemas support limited data integrity
no inherent ability to define and manage real integrity constraints
very basic primitive data type checking or referential integrity checking possible
becomes a requirement challenge for the tools or programs that populate and access the data store
challenge for the labor intensive quality assurance efforts to vet multiple error conditions
The problem of localization
Localization is the process of gathering, collecting and concentrating data from disparate data sources into a common local data store
the same source data may be localized redundantly by many systems

Benefits of Semantic Technology
Knowledge is open and represented by an ontology
an ontology can be characterized as a knowledge schema
provides a conceptual framework that classifies entities and their relationships to one another
includes a set of integrity rules that govern the relationships between entities
Semantic Technology Data Definition and Access Patterns TBox (terminology) ABox (assertions) New Data Entity Ontology / Semantic Schema Physical Database Some Business Rules Added to Ontology Application Software Some Inferred Data Some Business Rules Removed from Code Physical Format Unchanged after New Data Entity Added Access Update Define

Benefits of Semantic Technology (continued)
Data organization is decoupled from the schema
semantic schema is independent from the physical organization of the data
while the schema may require change, the underlying objects and data instances described by the ontology do not need to physically change for the new knowledge relationships to be realized
semantic capabilities can offer faster time to market opportunities for projects; at potentially lower costs, due to the expected reduction in labor intensive tasks.
Inferencing creates new knowledge
ability to use rules asserted about classes in order to generate a super-set of facts that is logically derived from a sub-set of facts, to arrive at a conclusion

Defines meaning of data
use of standardized semantic vocabulary
relationships of data
link analysis that traverses network graph of relationships
Enables data integration across heterogeneous silos
accepts the notion that data representations of the same fact can be diverse and heterogeneous as long as the meaning is tied together by an ontology (owl:sameAs)
No need to centralize data, just go to the source(s).
Utilizes “ reasoners ” to ensure data integrity
flags contradictions
guarantees consistent information
provides automatic data integrity checking

All semantic data can be Web addressable
every resource and every semantic language construct can be configured as a Web addressable URI.
Enables Web 3.0 “The Semantic Web”
machine understanding of Web content – intelligent agents
ubiquitous connectivity – every resource is a URL
knowledge centric patterns of computing – via ontologies
universally translated via self-describing ontology.
virtualized infrastructure and everything as a service (XaaS)

Overview of Semantic Technology

Origins
Philosophical Origins:
Deductive Logic - Aristotle
Epistemology - Study of knowledge
Ontology - Study of Being, Existence,
Reality, Nature of Things
Ontology (Computer Science)
Knowledge representation so that machines as well as people can commonly understand the meaning of data in order to accomplish tasks.
Knowledge is represented as a set of taxonomic classes, with relations and properties
Ontology is a specification of a conceptualization [Gruber]
Aristotle

Semantic Ontology Model
Small step forward towards reducing data chaos
Based upon Description Logic
A symbolic logic that allows reasoning about properties that are shared by many objects through the use of variables
Mathematically verifiable
Describes domains in terms of:
Concepts (classes)
Roles (relationships, properties)
Individuals (instances)
Subject (domain) Predicate (property) Object (range) RDF Triples/ Statements Aligns linguistically with how we think and speak Jackson Pollock “ Convergence ”

Basic Principles of Semantic Technology
Open view of the Truth
Closed World Assumption (CWA) – Any statement that is Not known to be True is therefore False. (Conventional Databases: If it is not in the database it doesn’t exist )
Open World Assumption (OWA) – A statement is False only if it is known to be False. Web Ontology allows incomplete data. Designed for inferencing, search, informed answers.
Monotonic Logic
Adding a new fact doesn’t invalidate previous facts or conclusions. (A person may live in many places).
Unique Name Assumption Not Supported
Unless specifically stated, any two instances might refer to the same thing i.e. doesn’t assume that because two individuals have different names, that they are not the same person

W3C Semantic Technology Languages
RDF – Resource Description Framework
RDFS – RDF Schema
OWL – Web Ontology Language
SPARQL – SPARQL Protocol and RDF Query Language
SWRL – Semantic Web Rules Language – rules that can be applied to RDF graphs
RIF – Rules Interchange Format
GRDDL – Gleaning Resource Descriptions from Dialects of Languages
POWDER - Protocol for Web Description Resources
GRDDL/XSLT Transform W3C Semantic Language Stack OWL SPARQL RDFS SWRL (RIF) RDF GRDDL POWDER XML URI

Foundational Concepts based on Description Logic
Class – a concept, a resource, a thing, a set, a collection of elements with similar properties.
:Person rdf:type owl:Class
Individual – instance that belongs to one or more classes. A member of a set
:David_Newman rdf:type :Person
Properties – describes the relationships between individuals.
A property is also a class in its own right
Resembles language constructs, how we think
:subject :predicate :object = {domain property range}
Object Properties – range of property is another class
:Service :hasOperation : Operation
Datatype Property – range of property is a data primitive, e.g. literal value, number, string
:Person :hasName “David Newman”

Assertions
Equivalence – asserts that two classes are the same
Every individual member of one class is also a member of the equivalent class
Class equivalence :TeamMember owl:EquivalentClass :Employee
Property equivalence :EmployedBy owl:EquivalentClass :WorksFor
Individual equivalence :David_Newman owl:SameAs :Dave_Newman
Subsumption – asserts that if an individual is a member of a class, it is also a member of its superclass.
:TeamMember :rdfs:subClassOf :Person
Class inheritance is transitive. (A - > B -> C), A -> C
A class inherits all of the attributes or properties of its superclass
Disjointness – asserts that two things are different.
Disjoint classes cannot have members in common
:Religious owl:disjointWith :Atheist
OWA assumes that things are the same unless told otherwise

Property Expressions
Functional – asserts that a property can have only one unique value for each instance.
:BiologicalMother rdf:type owl:FunctionalProperty
Inverse – asserts the property that is the reverse of the stated property. : Child owl:inverseOf :Parent .
Symmetric – asserts that a property holds true even when the subject and object are reversed
:Sibling rdf:type owl:SymmetricProperty
Transitive – asserts that if A has a relation to B, and B has a relation to C, then A has a relation to C.
:Ancestor rdf:type owl:TransitiveProperty

Complex Classes
Intersection (And) – class that contains all of the individuals that are common to all classes in the intersection
MainframeMQApp = intersectionOf(MQApp, MainframeApp)
Union (Or) – class that includes all members specified in the union
SFOAirlines = unionOf(UnitedAirlines, AmericanAirlines, etc)
Complement (Not) – class that includes all members that do not belong to a specific class
Vegetarian = complementOf(MeatEater)
Restriction – conditions that specify membership in a class. Reasoner determines whether an individual is a member of a class based upon predefined rules. Constrains the set of possible values or ranges for a property.
TierOneApplication = restriction(onProperty(hasTier), hasValue(TierOne))

Semantic Technology Providers and Adopters

(Some) Providers of Semantic Technology Pellet RacerPro Sesame OWLAPI Languages Ontology Editors Triple Stores Middleware Reasoners

(Some) Adopters of Semantic Technology

Semantic Applications for Financial Services
Fraud Detection
requires advanced capabilities for pattern matching, event correlation and link analysis
Know Your Customer (KYC)
regulations require financial organizations to assimilate diverse information about their customers from multiple sources
Asset and IT Portfolio Management
requires localization and integration of data from multiple sources
Customer Integration
requires a 360 degree view of the customer must be assembled
Personalization and Cross-Sell
requires a 360 degree view of the customer must be assembled
Records Management and eDiscovery
requires categorizing, searching and accessing structured and unstructured content

Semantic Applications for Financial Services (continued)
Service Oriented Architecture and Service Discovery
requires a canonical data schema that can auto-translate data content from one interface protocol to another, increasing the level of interoperability and reducing the need to continually version changes to Web service message interfaces
requires capability to advertise and locate service interfaces defined by a Service Registry
Logging and Monitoring
requires recording and monitoring of data that is often highly heterogeneous and diverse
Business Intelligence and Analytics
requires ability to access distributed disparate data and perform complex queries and link analysis
Market Intelligence for Investment Analytics
requires ability to scan the Web, parse RSS news feeds, and other sources, to identify, in real time, subjects of interest to an organization

Implications for Enterprise Architecture and Data Management Organizations
Enterprise Ontology, Standards and Governance
Upper Ontologies
Line of Business Ontologies
Federation of ontologies
Mission:
Manage and provide standards and quality control for enterprise semantic content
Limit risk of siloed ontologies
Utilize an enterprise Ontology Repository
Enterprise, federated, collaborative RDF/OWL repository [OpenOntologyRepository Initiative]

Recommended Semantic Technology Books and Articles

Recommended Books

The Power of Semantic Technology: Mind over Meta Article in Data Strategy Journal Spring 2009

Semantic Applications for Financial Services , FSTC Innovator: The Journal for Financial Services Technology Leaders , Volume 2, Issue 7, October 2009
Article in FSTC Innovator Journal

Follow-up or Questions
Email: [email_address]
Phone: 415 371-3188

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Semantics in Financial Services -David Newman

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