W3C WWW2004: Using the W3C Standard OWL in Adaptive Business Solutions

1. W3C WWW2004: Using the W3C Standard OWL in Adaptive Business Solutions Updated: MAY-2004

2. Today’s Agenda • Network Inference and the Semantic Web • Semantic Web Business Case • Core Adaptive Enterprise Use Cases: • Business Inferencing • Semantic Data Integration • Adaptive Enterprise Software Solutions • Fortune 500, Financial Chart of Accounts Management • NATO Country, Battlespace Awareness Desktop • Startup, Healthcare Patient Care System • Top 5 Reasons Why OWL Matters • Top 5 Reasons Why Description Logics Matter

3. NI and the Semantic Web • Network Inference is a Semantic Web innovator: • Prof. Ian Horrocks is a key contributor to OWL • Dr. Deborah McGuiness is a key contributor to OWL • Rob Shearer is a key member of the RDF Data Access WG • Network Inference, in 2003, unveils the first commercial OWL inference platform and successfully deploys with several customers. • Network Inference is committed to building Adaptive Enterprise software solutions using Semantic Web specifications and technologies • The greatest business impact, and adoption potential, will be to assist companies by lowering costs, and to improve adaptive capabilities of traditional enterprise deployments.

4. The Business Case • Who cares that it is “Semantic Web?” • Usually not our customers… • From a business point of view: • It can drastically lower operational costs • It provides powerful adaptive (new) business capabilities • It enables automation of business activity (standardized) • Because the underlying technology can: • Eliminate proprietary, non-interoperable metadata • Enable Machine interpretability of semantics (vs. syntax) • Create “reasonable” metadata about architecture layers

5. Use Case: Business Inferencing • What is it, and why should I care? • Business Inferencing is machine visibility into operational data, semantics, and business rules • Previously, any comparable capabilities were via highly proprietary metadata markup embedded inside tools • Business Inferencing enables dynamic applications to reason with and reclassify corporate data • Thus enabling machine access to business knowledge – automated use of all data and rules – instance data too. • It is used as a platform for application development • Replaces the business rules tier and manages business vocabularies at the infrastructure level – saves $$$

6. Use Case: Semantic Data Integration • What’s different, why can’t the established vendors simply add-in these capabilities? • Semantic Data Integration is the use of ontology as a mediating vocabulary for disparate underlying sources – a virtual hub and spoke • Unlike previous “business object” or “bus” style approaches, ontologies are conceptual languages at a higher abstraction – they don’t have to map 1:1 with underlying systems • Most vendors are committed to their data architectures, OWL is best used in the “core” – not as an “add-on” to an existing COTS product. • Full automation will not come “for free” with simple plug-ins, however, dramaticimprovements are achievable

7. Use Case: Semantic Web Services* • Why is “meaning” important in web services, SOA, and grid computing? • Avoid transformation code between data sets • Unambiguously capture service profiles • Enable dynamic discovery of services • Use reasoners to locate services in “yellow pages” • Enable dynamic collaboration of services • Use reasoners to infer service descriptions and capabilities • Enable rich, automatic, service orchestration • Process layer will be far more automated with semantics * Not a current customer deployment from NI

8. Fortune 500 Customer • Business Problem: Costly, untimely reporting of sales in a chart of accounts • Solution: OWL-driven adaptive platform for the allocation of unit sales and application of automated business rules MarketSegments Product Classifications Business Inference Platform Financial Analysts

9. NATO Country Customer • Business Problem: Inflexible IT systems prohibit robust visibility to changing battlespace IT systems • Solution: Easy XQuery access (with built in class and instance level inference) to intelligence data from disparate sources – enabling visibility into rapidly changing data, classifications, and rules. OWL RDF WebServices XQuery Operational Systems M e d i a t i o n IntelligenceDatabases DataQuality Business Inference

10. Healthcare Startup Customer • Business Problem: Costly adaptations to patient knowledge base with rapidly changing classifications • Solution: Business inferencing solution automatically reclassifies complex knowledge structures on-the-fly Web Portal Nurses, Doctors X-Query Interface Patient Families A-Box(inference) T-Box (inference) Symptoms & Resources

11. Why Description Logics? • Consistency – query results, across vendor implementations and instances, should be consistent • Performance – Although performance metrics depend on model constructs, OWL-DL supports highly optimized inference algorithms • Predictable – semantics are mathematically decidable within the model, reasoning is finite • Foundational – provides a baseline inside applications for layered semantic models • Reliability – if the answer to a query is implied by any of the model data, it will be found – guaranteed.

12. Top 5 Reasons for OWL • Loose-coupling – semantics may be decoupled from the application code (or parsing algorithms) • Machine-actionable – automated decisions can be made from interpretable inferences • Highly expressive – can capture core elements of EER, UML, and frame-based systems • Precision – language checking available to prevent inconsistent/contradictory model semantics • Fun acronym! – OWL is named for the owl in Winnie the Pooh, who spelled his name WOL

13. WWW2004, New York With the W3C’s standard OWL, everybody can finally enable truly adaptive, standard application architectures. jeff.pollock@networkinference.com

14. WWW2004Backup Slides: why owl matters to IT systems…?

15. Why OWL Matters – Reason #5 • Semantics are loosely-coupled • Characteristic • OWL ontologies are schema representations, independent of application code and RDF models • OWL markup is easily stored and referenced in a loosely-coupled registry/repository style architecture • Benefits • Semantics are late-bound, thereby supporting an evolutionary – not static – network model for changing data meanings and business rules • Semantics may be easily federated in simple markup • Semantics may be loosely-coupled to instance data

16. Why OWL Matters – Reason #4 • Semantics are machine-actionable • Characteristic • OWL is syntax (not graphical) grounded in XML & RDF • OWL uses consistent, standard schema semantics • Supports well-scoped classes, properties (class relationships), instances and instance relationships • Benefits • Parsers, modelers, reasoners, and transformers are available today • DL guarantees 100% decidability and computational completeness – any two DL reasoners should come up with the same (all possible) answers to queries

17. Why OWL Matters – Reason #3 • OWL is more expressive • Characteristic • Rich set of built-in simple properties, property characteristics and restrictions • Not just hierarchical or taxonomic (like most XML) • Not just two-dimensional (like ER/RDBMS) • Allowable, functional, multiple inheritance • Benefit • More closely models “real-world” • Axioms may be used to model rules directly into the model (compare with OCL-type approaches)

18. Why OWL Matters – Reason #2 • OWL is more precise • Characteristic • Relationships are atomic and unambiguous • Unlike UML/ER/XML, properties have stand-alone meaning • Disallows over-riding attributes (no semantic ambiguity) • DL enforces consistency • Within a context, semantics can be 100% unambiguous • Benefit • Reasoners can accommodate uncertain/unknown data • Both explicit and implicit facts are available via a mediated query capability

19. Why OWL Matters – Reason #1 • OWL is a FUN acronym (and apt!) • Characteristic • OWL = wisdom • OWL is named for the owl in Winnie the Pooh (who spelled his own name “WOL”) • Benefit • Makes peoplesmile and laugh!

20. The End. jeff.pollock@networkinference.com