Querying Dynamic and Context-Sensitive Metadata in Semantic Web
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Transcript of Querying Dynamic and Context-Sensitive Metadata in Semantic Web
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
What lies beneath abstract models?
How Intelligent Agent manages data?
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
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
Smart Resource 2005 Scenario (3 scenes) Smart Resource 2005 Scenario (3 scenes)
““Expert”Expert”
““Service”Service”
Labelled data
Labelled data
Diagnostic model
Que
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g di
agno
stic
Que
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g di
agno
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resu
ltsre
sults
Labelled data
Labelled data
Wat
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qu
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dia
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ic d
ataLa
belle
d da
ta
Labe
lled
data
History data
““Device”Device”
Querying data for
learning
Learning sample and
Learning sample and
Querying diagnostic results
Querying diagnostic results
““Knowledge Transfer form Expert to Service”Knowledge Transfer form Expert to Service”
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).
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
StatementStatement
SSSSSSPPPPPP
rdf:subject rdf:object
rscdfs:predicate
rscdfs:trueInContext
OOOOOO
rscdfs:Context_SR_Container
Use Case Example
• Query: “Select Statements corresponding to state of some device”
State Time Property ValueT1 temperature 70
roundsPerMinute 1500
T2 temperature 80
roundsPerMinute 1700
T3 temperature 83
roundsPerMinute 1750
Device 1 Sensors
Contextual Data Example
Temperature Statement 1Temperature Statement 1
Device1Device1temperatureCelsiustemperatureCelsius
rdf:subject rdf:object
rscdfs:predicate
rscdfs:trueInContext
Value:70Value:70
Unit:CelsiusUnit:Celsius
rscdfs:Context_SR_Container
StatementStatementStatementStatement
rdf:subject rdf:objectrscdfs:predicate
WorldWorld hasTimehasTime 07.06.05T11:33:1207.06.05T11:33:12
Rotation Statement 1Rotation Statement 1
Device1Device1roundsPerMinuteroundsPerMinute
rdf:subject rdf:object
rscdfs:predicate
rscdfs:trueInContext
Value:1500Value:1500
Unit:rpmUnit:rpm
rscdfs:Context_SR_Container
StatementStatementStatementStatement
rdf:subject rdf:objectrscdfs:predicate
WorldWorld hasTimehasTime 07.06.05T11:33:1207.06.05T11:33:12
Both containers refer to the same time statement
State Statement Example
State StatementState Statement
Device1Device1contOnt:resourceStatecontOnt:resourceState
rdf:subject
rdf:objectrscdfs:predicate
rscdfs:trueInContext
rscdfs:Context_SR_Container
rscdfs:SR_Container
Temperature Statement 1Temperature Statement 1Temperature Statement 1Temperature Statement 1
Rotation Statement 1Rotation Statement 1Rotation Statement 1Rotation Statement 1
Template StatementTemplate StatementTemplate StatementTemplate Statement
rdf:subject
rscdfs:predicateWorldWorld
measOnt:resourceMeasurementmeasOnt:resourceMeasurement
rscdfs:trueInContext
StatementStatementStatementStatement
rdf:subject rdf:objectrscdfs:predicate
WorldWorld hasTimehasTime 07.06.05T11:33:1207.06.05T11:33:12
rscdfs:Context_SR_Container
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)
Statement ID Unit Value
Temperature Statement 1 Temperature 70
Rotation Statement 1 roundsPerMinute 1500
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RDQL-patterns: Modularity
PatternInput Output
Composed Pattern
Input OutputPattern Output PatternInput Output PatternInput
Use cases for pattern application in Agent Systems
hasGoals
rscdfs:predicaterdf:subject
Agent
rscdfs:SR_Statement
rscdfs:Context_SR_Container
rscdfs:trueInContext
rdf:object
rscdfs:SR_Container
Goal Statement 1Goal Statement 1Goal Statement 1Goal Statement 1
Goal Statement 2Goal Statement 2Goal Statement 2Goal Statement 2
…
Use cases for pattern application in Agent Systems
hasBehaviour
rscdfs:predicaterdf:subject
Agent
Behaviour_Statement
rscdfs:trueInContext
rdf:object
Behaviour_Container
Buy TicketsBuy TicketsBuy TicketsBuy Tickets
StatementStatementStatementStatement
rdf:subject rdf:objectrscdfs:predicate
AgentAgent hashas MoneyMoney
rscdfs:Context_SR_Container
Agent ArchitectureAgent Architecture
Resource Resource HistoryHistory
Ontology
Templates
Roles Goals
Behaviour rules
Resource Resource AgentAgent
Behaviour description
Templates
Executable Executable modules or modules or
Web ServicesWeb Services
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
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)
Welcome to IASW-2005 conferenceWelcome to IASW-2005 conference
http://www.cs.jyu.fi/ai/OntoGroup/IASW-2005/