1. 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 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 SmartResource 1 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. Smart Resource 2005 Scenario (3 scenes) “Expert” “Service” Labelled data Diagnostic model Querying diagnostic results Labelled data Watching and querying diagnostic data Labelled data History data “Device” Querying data for learning Learning sample and Querying diagnostic results “Knowledge Transfer form Expert to Service”

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 Statement SSS PPP rdf: subject rdf: object rscdfs: predicate rscdfs: trueInContext OOO rscdfs:Context_SR_Container

8. Use Case Example Query: “ Select Statements corresponding to state of some device ” State TimePropertyValue T1temperature70 roundsPerMinute1500 T2temperature80 roundsPerMinute1700 T3temperature83 roundsPerMinute1750 Device 1 Sensors

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

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

11. RDQL-patterns SELECT ?ValueStatements, ?NumUnits, ?NumValues WHERE (,, ?StateContainer), (?StateContainer,, ?ValueStatements), (?ValueStatements,, ?NumValueInstances), (?NumValueInstances,,?NumValues), (?NumValueInstances,, ?NumUnits) Statement IDUnitValue Temperature Statement 1Temperature70 Rotation Statement 1roundsPerMinute1500 * * * * * * * * *

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

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

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

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

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 (R GB DF) Querying patterns for R GB DF 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/