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Querying Dynamic and Context-Sensitive Metadata in Semantic Web

Querying Dynamic and Context-Sensitive Metadata in Semantic Web. Sergiy Nikitin Industrial Ontologies Group 1 University of Jyväskylä Finland. Article Authors: Sergiy Nikitin Vagan Terziyan Yaroslav Tsaruk Andriy Zharko.

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Querying Dynamic and Context-Sensitive Metadata in Semantic Web

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  1. Querying Dynamic and Context-Sensitive Metadata in Semantic Web Sergiy Nikitin Industrial Ontologies Group1 University of Jyväskylä Finland Article Authors: Sergiy Nikitin Vagan Terziyan Yaroslav Tsaruk Andriy Zharko 1 – Industrial Ontologies Group web-site: http://www.cs.jyu.fi/ai/OntoGroup

  2. What lies beneath abstract models? How Intelligent Agent manages data?

  3. Contents • Story of contextual data querying problem • Contextual Data in Semantic Web • RDQL patterns • Use cases for pattern application in Agent Systems • Conclusions • Further Work

  4. Introduction • Dynamic, semantically rich data usually contains contextual elements describing conditions under which the data is relevant, useful and up-to-date • The problem of querying contextual data appeared as a first-year challenge of SmartResource1 project • Project wider objective is: • To combine the emerging Semantic Web, Web Services, Peer-to-Peer, Machine Learning and Agent technologies for the development of a global and smart maintenance management environment, to provide Web-based support for the predictive maintenance of industrial devices by utilizing heterogeneous and interoperable Web resources, services and human experts 1 - SmartResource project web-site: http://www.cs.jyu.fi/ai/OntoGroup/projects.htm

  5. Labelled data Labelled data Smart Resource 2005 Scenario (3 scenes) “Knowledge Transfer form Expert to Service” “Expert” Labelled data Watching and querying diagnostic data Querying diagnostic results “Device” “Service” Labelled data History data Querying data for learning Learning sample and Querying diagnostic results Diagnostic model

  6. SmartResource project • The objective of project stage 1 (year 2004): • Define Semantic Web-based framework for unification of maintenance data and interoperability in maintenance system • R&D tasks included: • Development of generic semantic adapter mechanism (General Adaptation Framework) • Supporting Ontology (Resource State/Condition Description Framework) for different types of industrial resources: devices, software components (services) and humans (operators or experts).

  7. Contextual Data • RscDF (Resource State/Condition Description Framework) provides additional constructions on top of RDF-Schema • RscDF is fully compliant with RDF • Contextual construction for Statement rscdfs:trueInContext rscdfs:Context_SR_Container Statement rdf:object rdf:subject rscdfs:predicate OOO SSS PPP

  8. Use Case Example • Query: “Select Statements corresponding to state of some device” Device 1 Sensors

  9. Contextual Data Example rscdfs:trueInContext rscdfs:Context_SR_Container Temperature Statement 1 Statement rdf:object rdf:subject rdf:subject rdf:object rscdfs:predicate rscdfs:predicate World 07.06.05T11:33:12 hasTime Value:70 Unit:Celsius Device1 temperatureCelsius Both containers refer to the same time statement rscdfs:trueInContext rscdfs:Context_SR_Container Rotation Statement 1 Statement rdf:object rdf:subject rdf:subject rdf:object rscdfs:predicate rscdfs:predicate World 07.06.05T11:33:12 hasTime Device1 Value:1500 Unit:rpm roundsPerMinute

  10. contOnt:resourceState State Statement Example rscdfs:trueInContext rscdfs:Context_SR_Container State Statement rscdfs:Context_SR_Container rdf:subject rscdfs:predicate rdf:object Statement Device1 rdf:subject rdf:object rscdfs:predicate World 07.06.05T11:33:12 hasTime rscdfs:SR_Container rscdfs:trueInContext Temperature Statement 1 Template Statement rdf:subject Rotation Statement 1 rscdfs:predicate World measOnt:resourceMeasurement

  11. RDQL-patterns SELECT ?ValueStatements, ?NumUnits, ?NumValues WHERE (<StateStmtID>, <rdf:object>, ?StateContainer), (?StateContainer, <rscdfs:member>, ?ValueStatements), (?ValueStatements, <rdf:object>, ?NumValueInstances), (?NumValueInstances, <rscdfs:value>,?NumValues), (?NumValueInstances, <rscdfs:unit>, ?NumUnits) * * * * * * * * *

  12. Pattern Output Input Pattern Input Pattern Output Composed Pattern Input Output RDQL-patterns: Modularity Pattern Output Input

  13. Use cases for pattern application in Agent Systems rscdfs:Context_SR_Container rscdfs:trueInContext rscdfs:SR_Statement rdf:object rdf:subject rscdfs:predicate rscdfs:SR_Container Agent Goal Statement 1 hasGoals Goal Statement 2 …

  14. Use cases for pattern application in Agent Systems rscdfs:Context_SR_Container Statement rscdfs:trueInContext rdf:subject rdf:object rscdfs:predicate Agent Money has Behaviour_Statement rdf:object rdf:subject rscdfs:predicate Behaviour_Container Agent Buy Tickets hasBehaviour

  15. Resource Agent Agent Architecture Ontology Roles Goals Templates Behaviour rules Resource History Templates Executable modules or Web Services Behaviour description

  16. Conclusions • Storing and managing context-enabled data via RDF storages is complicated and routine task • Repeating querying procedures can be organized into reusable querying patterns • Patterns can consist of other patterns, thus pattern ontology can be developed to represent these relationships • Patterns correspond to Properties. Property by its range value defines classes of objects which can be referred, hence these objects correspond to certain common structure

  17. Further work • Further development of Resource Goal/Behaviour Description Framework (RGBDF) • Querying patterns for RGBDF • Deeper analysis of Pattern Ontology (how to describe relationships between patterns, how they correlate with Properties)

  18. Welcome to IASW-2005 conference http://www.cs.jyu.fi/ai/OntoGroup/IASW-2005/

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