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?

http://www.bitstorm.org/gameoflife/code/gameoflife-uml.png

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

ryin

g di

agno

stic

Que

ryin

g di

agno

stic

resu

ltsre

sults

Labelled data

Labelled data

Wat

chin

g a

nd

qu

eryi

ng

dia

gn

ost

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


T3 temperature 83


Device 1 Sensors

Contextual Data Example

Temperature Statement 1Temperature Statement 1

Device1Device1temperatureCelsiustemperatureCelsius


rscdfs:predicate


Value:70Value:70

Unit:CelsiusUnit:Celsius


StatementStatementStatementStatement

rdf:subject rdf:objectrscdfs:predicate

WorldWorld hasTimehasTime 07.06.05T11:33:1207.06.05T11:33:12

Rotation Statement 1Rotation Statement 1

Device1Device1roundsPerMinuteroundsPerMinute


rscdfs:predicate


Value:1500Value:1500

Unit:rpmUnit:rpm





Both containers refer to the same time statement

State Statement Example

State StatementState Statement

Device1Device1contOnt:resourceStatecontOnt:resourceState

rdf:subject

rdf:objectrscdfs:predicate



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






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

*

*

*

*

*

*

*

*

*

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



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


rdf:object

Behaviour_Container

Buy TicketsBuy TicketsBuy TicketsBuy Tickets



AgentAgent hashas MoneyMoney


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/

http://www.cs.jyu.fi/ai/OntoGroup/IASW-2005/main.html

Documents

Querying Dynamic and Context-Sensitive Metadata in Semantic Web