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Semantics in Financial Services -David Newman


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David Newman serves as a Senior Architect in the Enterprise Architecture group at Wells Fargo Bank. He has been following semantic technology for the last 3 years; and has developed several business ontologies. He has been instrumental in thought leadership at Wells Fargo on the application of Semantic Technology and is a representative of the Financial Services Technology Consortium (FSTC)on the W3C SPARQL Working Group.

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Semantics in Financial Services -David Newman

  1. 1. 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
  2. 2. Agenda <ul><li>The Case for Semantic Technology </li></ul><ul><ul><li>Key Enterprise Business and IT Drivers for Semantic Technology </li></ul></ul><ul><ul><li>Limitations of Conventional Integration and Database Technologies </li></ul></ul><ul><ul><li>Benefits of Semantic Technology </li></ul></ul><ul><li>Overview of Semantic Technology </li></ul><ul><ul><li>Origins, Ontology Model, Basic Principles, Languages, Basic Concepts </li></ul></ul><ul><li>Semantic Technology Providers and Adopters </li></ul><ul><li>Semantic Applications for Financial Services </li></ul><ul><ul><li>Use Cases: Business and Technology Perspectives </li></ul></ul><ul><ul><li>Implications for Enterprise Architecture and Data Management Organizations </li></ul></ul><ul><li>Recommended Semantic Technology Books and Articles </li></ul>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
  3. 3. The Case for Semantic Technology
  4. 4. Key Business and IT Drivers for Semantic Technology <ul><li>Problem: </li></ul><ul><ul><li>Enterprise Data Fragmentation as a result of: </li></ul></ul><ul><ul><ul><li>incompatible data meanings, definitions, vocabulary. </li></ul></ul></ul><ul><ul><ul><li>multiple incompatible physical data formats and structures </li></ul></ul></ul><ul><ul><ul><li>proliferation of unstructured data </li></ul></ul></ul><ul><ul><ul><li>multiple heterogeneous data stores across multiple siloed organizations with redundant data </li></ul></ul></ul><ul><li>Impact: </li></ul><ul><ul><li>Results in less than optimum information/knowledge quality </li></ul></ul><ul><ul><li>Dilutes effectiveness and business value of data </li></ul></ul><ul><ul><li>Data integration is costly and difficult to achieve </li></ul></ul><ul><ul><li>Negatively impacts enterprise bottom line and increases risk </li></ul></ul><ul><li>Goal: </li></ul><ul><ul><li>Reintegrate the fragmented meanings and instances of data </li></ul></ul>
  5. 5. Limitations of Conventional Integration and Database Technologies <ul><li>Knowledge is encapsulated in opaque software </li></ul><ul><ul><li>Challenge to normalize disparate data from multiple sources </li></ul></ul><ul><ul><li>Often represented in proprietary software and programs </li></ul></ul><ul><ul><li>Hard to access, should be an institutional asset </li></ul></ul>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
  6. 6. Limitations of Conventional Integration and Database Technologies (continued) <ul><li>Data schemas reflect limited knowledge </li></ul><ul><ul><li>conceptual model or framework used to describe a pattern or a set of data structures </li></ul></ul><ul><ul><li>segregation between the schematic structure of the data and programmatic logic or rules that are invoked at runtime to classify data </li></ul></ul><ul><ul><li>limited to data structures and data constraints, but not to richer categorizations and rules </li></ul></ul><ul><li>Data organization is tightly coupled with the schema </li></ul><ul><ul><li>physical representation of the data, is dependent upon the content of the schema that defines the data </li></ul></ul><ul><ul><li>change to the schema often requires, or results in, change to the physical representation of the data </li></ul></ul>
  7. 7. Limitations of Conventional Integration and Database Technologies (c0ntinued) <ul><li>Schemas support limited data integrity </li></ul><ul><ul><li>no inherent ability to define and manage real integrity constraints </li></ul></ul><ul><ul><li>very basic primitive data type checking or referential integrity checking possible </li></ul></ul><ul><ul><li>becomes a requirement challenge for the tools or programs that populate and access the data store </li></ul></ul><ul><ul><li>challenge for the labor intensive quality assurance efforts to vet multiple error conditions </li></ul></ul><ul><li>The problem of localization </li></ul><ul><ul><li>Localization is the process of gathering, collecting and concentrating data from disparate data sources into a common local data store </li></ul></ul><ul><ul><li>the same source data may be localized redundantly by many systems </li></ul></ul>
  8. 8. Benefits of Semantic Technology <ul><li>Knowledge is open and represented by an ontology </li></ul><ul><ul><li>an ontology can be characterized as a knowledge schema </li></ul></ul><ul><ul><li>provides a conceptual framework that classifies entities and their relationships to one another </li></ul></ul><ul><ul><li>includes a set of integrity rules that govern the relationships between entities </li></ul></ul>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
  9. 9. Benefits of Semantic Technology (continued) <ul><li>Data organization is decoupled from the schema </li></ul><ul><ul><li>semantic schema is independent from the physical organization of the data </li></ul></ul><ul><ul><li>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 </li></ul></ul><ul><ul><li>semantic capabilities can offer faster time to market opportunities for projects; at potentially lower costs, due to the expected reduction in labor intensive tasks. </li></ul></ul><ul><li>Inferencing creates new knowledge </li></ul><ul><ul><li>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 </li></ul></ul>
  10. 10. Benefits of Semantic Technology (continued) <ul><li>Defines meaning of data </li></ul><ul><ul><li>use of standardized semantic vocabulary </li></ul></ul><ul><ul><li>relationships of data </li></ul></ul><ul><ul><li>link analysis that traverses network graph of relationships </li></ul></ul><ul><li>Enables data integration across heterogeneous silos </li></ul><ul><ul><li>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) </li></ul></ul><ul><ul><li>No need to centralize data, just go to the source(s). </li></ul></ul><ul><li>Utilizes “ reasoners ” to ensure data integrity </li></ul><ul><ul><li>flags contradictions </li></ul></ul><ul><ul><li>guarantees consistent information </li></ul></ul><ul><ul><li>provides automatic data integrity checking </li></ul></ul>
  11. 11. Benefits of Semantic Technology (continued) <ul><li>All semantic data can be Web addressable </li></ul><ul><ul><li>every resource and every semantic language construct can be configured as a Web addressable URI. </li></ul></ul><ul><li>Enables Web 3.0 “The Semantic Web” </li></ul><ul><ul><li>machine understanding of Web content – intelligent agents </li></ul></ul><ul><ul><li>ubiquitous connectivity – every resource is a URL </li></ul></ul><ul><ul><li>knowledge centric patterns of computing – via ontologies </li></ul></ul><ul><ul><li>universally translated via self-describing ontology. </li></ul></ul><ul><ul><li>virtualized infrastructure and everything as a service (XaaS) </li></ul></ul>
  12. 12. Overview of Semantic Technology
  13. 13. Origins <ul><li>Philosophical Origins: </li></ul><ul><ul><li>Deductive Logic - Aristotle </li></ul></ul><ul><ul><li>Epistemology - Study of knowledge </li></ul></ul><ul><ul><li>Ontology - Study of Being, Existence, </li></ul></ul><ul><ul><li>Reality, Nature of Things </li></ul></ul><ul><li>Ontology (Computer Science) </li></ul><ul><ul><li>Knowledge representation so that machines as well as people can commonly understand the meaning of data in order to accomplish tasks. </li></ul></ul><ul><ul><li>Knowledge is represented as a set of taxonomic classes, with relations and properties </li></ul></ul><ul><ul><li>Ontology is a specification of a conceptualization [Gruber] </li></ul></ul>Aristotle
  14. 14. Semantic Ontology Model <ul><li>Small step forward towards reducing data chaos </li></ul><ul><li>Based upon Description Logic </li></ul><ul><ul><li>A symbolic logic that allows reasoning about properties that are shared by many objects through the use of variables </li></ul></ul><ul><ul><li>Mathematically verifiable </li></ul></ul><ul><li>Describes domains in terms of: </li></ul><ul><ul><li>Concepts (classes) </li></ul></ul><ul><ul><li>Roles (relationships, properties) </li></ul></ul><ul><ul><li>Individuals (instances) </li></ul></ul>Subject (domain) Predicate (property) Object (range) RDF Triples/ Statements Aligns linguistically with how we think and speak Jackson Pollock “ Convergence ”
  15. 15. Basic Principles of Semantic Technology <ul><li>Open view of the Truth </li></ul><ul><ul><li>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 ) </li></ul></ul><ul><ul><li>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. </li></ul></ul><ul><li>Monotonic Logic </li></ul><ul><ul><li>Adding a new fact doesn’t invalidate previous facts or conclusions. (A person may live in many places). </li></ul></ul><ul><li>Unique Name Assumption Not Supported </li></ul><ul><ul><li>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 </li></ul></ul>
  16. 16. W3C Semantic Technology Languages <ul><li>RDF – Resource Description Framework </li></ul><ul><li>RDFS – RDF Schema </li></ul><ul><li>OWL – Web Ontology Language </li></ul><ul><li>SPARQL – SPARQL Protocol and RDF Query Language </li></ul><ul><li>SWRL – Semantic Web Rules Language – rules that can be applied to RDF graphs </li></ul><ul><li>RIF – Rules Interchange Format </li></ul><ul><li>GRDDL – Gleaning Resource Descriptions from Dialects of Languages </li></ul><ul><li>POWDER - Protocol for Web Description Resources </li></ul>GRDDL/XSLT Transform W3C Semantic Language Stack OWL SPARQL RDFS SWRL (RIF) RDF GRDDL POWDER XML URI
  17. 17. Foundational Concepts based on Description Logic <ul><li>Class – a concept, a resource, a thing, a set, a collection of elements with similar properties. </li></ul><ul><ul><li>:Person rdf:type owl:Class </li></ul></ul><ul><li>Individual – instance that belongs to one or more classes. A member of a set </li></ul><ul><ul><li>:David_Newman rdf:type :Person </li></ul></ul><ul><li>Properties – describes the relationships between individuals. </li></ul><ul><ul><li>A property is also a class in its own right </li></ul></ul><ul><ul><li>Resembles language constructs, how we think </li></ul></ul><ul><ul><li>:subject :predicate :object = {domain property range} </li></ul></ul><ul><ul><li>Object Properties – range of property is another class </li></ul></ul><ul><ul><ul><li>:Service :hasOperation : Operation </li></ul></ul></ul><ul><ul><li>Datatype Property – range of property is a data primitive, e.g. literal value, number, string </li></ul></ul><ul><ul><ul><li>:Person :hasName “David Newman” </li></ul></ul></ul>
  18. 18. Assertions <ul><li>Equivalence – asserts that two classes are the same </li></ul><ul><ul><li>Every individual member of one class is also a member of the equivalent class </li></ul></ul><ul><ul><ul><li>Class equivalence :TeamMember owl:EquivalentClass :Employee </li></ul></ul></ul><ul><ul><ul><li>Property equivalence :EmployedBy owl:EquivalentClass :WorksFor </li></ul></ul></ul><ul><ul><ul><li>Individual equivalence :David_Newman owl:SameAs :Dave_Newman </li></ul></ul></ul><ul><li>Subsumption – asserts that if an individual is a member of a class, it is also a member of its superclass. </li></ul><ul><ul><li>:TeamMember :rdfs:subClassOf :Person </li></ul></ul><ul><li>Class inheritance is transitive. (A - > B -> C), A -> C </li></ul><ul><ul><li>A class inherits all of the attributes or properties of its superclass </li></ul></ul><ul><li>Disjointness – asserts that two things are different. </li></ul><ul><ul><li>Disjoint classes cannot have members in common </li></ul></ul><ul><ul><ul><li>:Religious owl:disjointWith :Atheist </li></ul></ul></ul><ul><ul><li>OWA assumes that things are the same unless told otherwise </li></ul></ul>
  19. 19. Property Expressions <ul><li>Functional – asserts that a property can have only one unique value for each instance. </li></ul><ul><ul><li>:BiologicalMother rdf:type owl:FunctionalProperty </li></ul></ul><ul><li>Inverse – asserts the property that is the reverse of the stated property. : Child owl:inverseOf :Parent . </li></ul><ul><li>Symmetric – asserts that a property holds true even when the subject and object are reversed </li></ul><ul><ul><li>:Sibling rdf:type owl:SymmetricProperty </li></ul></ul><ul><li>Transitive – asserts that if A has a relation to B, and B has a relation to C, then A has a relation to C. </li></ul><ul><ul><li>:Ancestor rdf:type owl:TransitiveProperty </li></ul></ul>
  20. 20. Complex Classes <ul><li>Intersection (And) – class that contains all of the individuals that are common to all classes in the intersection </li></ul><ul><ul><li>MainframeMQApp = intersectionOf(MQApp, MainframeApp) </li></ul></ul><ul><li>Union (Or) – class that includes all members specified in the union </li></ul><ul><ul><li>SFOAirlines = unionOf(UnitedAirlines, AmericanAirlines, etc) </li></ul></ul><ul><li>Complement (Not) – class that includes all members that do not belong to a specific class </li></ul><ul><ul><li>Vegetarian = complementOf(MeatEater) </li></ul></ul><ul><li>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. </li></ul><ul><ul><li>TierOneApplication = restriction(onProperty(hasTier), hasValue(TierOne)) </li></ul></ul>
  21. 21. Semantic Technology Providers and Adopters
  22. 22. (Some) Providers of Semantic Technology Pellet RacerPro Sesame OWLAPI Languages Ontology Editors Triple Stores Middleware Reasoners
  23. 23. (Some) Adopters of Semantic Technology
  24. 24. Semantic Applications for Financial Services <ul><li>Fraud Detection </li></ul><ul><ul><li>requires advanced capabilities for pattern matching, event correlation and link analysis </li></ul></ul><ul><li>Know Your Customer (KYC) </li></ul><ul><ul><li>regulations require financial organizations to assimilate diverse information about their customers from multiple sources </li></ul></ul><ul><li>Asset and IT Portfolio Management </li></ul><ul><ul><li>requires localization and integration of data from multiple sources </li></ul></ul><ul><li>Customer Integration </li></ul><ul><ul><li>requires a 360 degree view of the customer must be assembled </li></ul></ul><ul><li>Personalization and Cross-Sell </li></ul><ul><ul><li>requires a 360 degree view of the customer must be assembled </li></ul></ul><ul><li>Records Management and eDiscovery </li></ul><ul><ul><li>requires categorizing, searching and accessing structured and unstructured content </li></ul></ul>
  25. 25. Semantic Applications for Financial Services (continued) <ul><li>Service Oriented Architecture and Service Discovery </li></ul><ul><ul><li>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 </li></ul></ul><ul><ul><li>requires capability to advertise and locate service interfaces defined by a Service Registry </li></ul></ul><ul><li>Logging and Monitoring </li></ul><ul><ul><li>requires recording and monitoring of data that is often highly heterogeneous and diverse </li></ul></ul><ul><li>Business Intelligence and Analytics </li></ul><ul><ul><li>requires ability to access distributed disparate data and perform complex queries and link analysis </li></ul></ul><ul><li>Market Intelligence for Investment Analytics </li></ul><ul><ul><li>requires ability to scan the Web, parse RSS news feeds, and other sources, to identify, in real time, subjects of interest to an organization </li></ul></ul>
  26. 26. Implications for Enterprise Architecture and Data Management Organizations <ul><li>Enterprise Ontology, Standards and Governance </li></ul><ul><ul><li>Upper Ontologies </li></ul></ul><ul><li>Line of Business Ontologies </li></ul><ul><ul><li>Federation of ontologies </li></ul></ul><ul><li>Mission: </li></ul><ul><ul><li>Manage and provide standards and quality control for enterprise semantic content </li></ul></ul><ul><ul><li>Limit risk of siloed ontologies </li></ul></ul><ul><li>Utilize an enterprise Ontology Repository </li></ul><ul><ul><li>Enterprise, federated, collaborative RDF/OWL repository [OpenOntologyRepository Initiative] </li></ul></ul>
  27. 27. Recommended Semantic Technology Books and Articles
  28. 28. Recommended Books
  29. 29. The Power of Semantic Technology: Mind over Meta Article in Data Strategy Journal Spring 2009
  30. 30. <ul><li>Semantic Applications for Financial Services , FSTC Innovator: The Journal for Financial Services Technology Leaders , Volume 2, Issue 7, October 2009 </li></ul><ul><li>Article in FSTC Innovator Journal </li></ul>
  31. 31. Follow-up or Questions <ul><li>Email: [email_address] </li></ul><ul><li>Phone: 415 371-3188 </li></ul>