W3C WWW2004: Using the W3C Standard OWL in Adaptive Business Solutions : W3C WWW2004: Using the W3C Standard OWL in Adaptive Business Solutions Updated: MAY-2004
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 Today’s Agenda
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. NI and the Semantic Web
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 The Business Case
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 $$$ Use Case: Business Inferencing
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, dramatic improvements are achievable Use Case: Semantic Data Integration
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 Use Case: Semantic Web Services* * Not a current customer deployment from NI
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 Fortune 500 Customer Market Segments Product
Classifications Business
Inference
Platform Financial Analysts
NATO Country Customer : 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. Web Services Operational
Systems Intelligence Databases OWL RDF XQuery Business
Inference Data Quality M
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Healthcare Startup Customer : Healthcare Startup Customer T-Box
(inference) A-Box (inference) X-Query Interface Business Problem: Costly adaptations to patient knowledge base with rapidly changing classifications
Solution: Business inferencing solution automatically reclassifies complex knowledge structures on-the-fly Patient Families Web Portal Nurses, Doctors Symptoms & Resources
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. Why Description Logics?
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 Top 5 Reasons for OWL
With the W3C’s standard OWL, everybody can finally enable truly adaptive, standard application architectures. : With the W3C’s standard OWL, everybody can finally enable truly adaptive, standard application architectures. jeff.pollock@networkinference.com WWW2004, New York
WWW2004 Backup Slides: : WWW2004 Backup Slides: why owl matters to IT systems…?
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 Why OWL Matters – Reason #5
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 Why OWL Matters – Reason #4
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) Why OWL Matters – Reason #3
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 Why OWL Matters – Reason #2
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 people smile and laugh! Why OWL Matters – Reason #1
The End. : The End. jeff.pollock@networkinference.com