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UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY. Matthew Williams [email protected]. OVERVIEW. Introduction. Motivation – the Semantic and Sensor Webs. UncertML overview. Use case – The INTAMAP project. Conclusions. MOTIVATION. The semantic and sensor webs. THE SENSOR WEB. - PowerPoint PPT Presentation
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UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY
Matthew [email protected]
OVERVIEW Introduction.
Motivation – the Semantic and Sensor Webs.
UncertML overview.
Use case – The INTAMAP project.
Conclusions.
MOTIVATIONThe semantic and sensor webs
THE SENSOR WEB
SENSOR WEB ENABLEMENT (SWE) Open Geospatial Consortium (OGC) initiative
Interoperability interfaces and metadata encodings.
Real time integration of heterogeneous sensor webs into the information infrastructure.
Current SWE standards Observations & Measurements SensorML SWE Common
No formal standard for quantifying uncertainty
<Quantity id="elevationAngle" fixed="false" definition="urn:ogc:def:scanElevationAngle">
<uom xlink:href="urn:ogc:unit:degree"/><quality><Tolerance definition="urn:ogc:def:tolerance2std"><value> -0.02 0.02 </value>
</Tolerance></quality><value> 25.3 </value>
</Quantity>
HOW UNCERTAINTY IS USED WITHIN THE SEMANTIC WEB
PR-OWL: a Bayesian Ontology Language for the Semantic Web: Extends OWL to allow probabilistic knowledge to
be represented in an ontology. Used for reasoning with Bayesian inference. Random variables are described by either a PR-
OWL table (discrete probability) or using a proprietary format.
Other standards looking at similar concepts: BayesOWL. FuzzyOWL.
What next?
A formal open standard for quantifying complex uncertainties Extend to allow continuous distributions More powerful reasoning, richer representations
UNCERTML
OVERVIEW
Split into three distinct packages (distributions, statistics & realisations).
DISTRIBUTIONS
<un:Distribution definition="http://dictionary.uncertml.org/distributions/gaussian"> <un:parameters> <un:Parameter definition="http://dictionary.uncertml.org/distributions/gaussian/mean"> <un:value>34.564</un:value> </un:Parameter> <un:Parameter definition="http://dictionary.uncertml.org/distributions/gaussian/variance"> <un:value>67.45</un:value> </un:Parameter> </un:parameters></un:Distribution>
UNCERTMLAn overview
WEAK VS. STRONG
Benefits Generic features
have generic properties – extensible
Drawbacks Validation becomes
less meaningful
Benefits Produces relatively
simple XML features
Drawbacks Not easily extended
– all domain features must be known a priori
Weak-typed Strong-typed
<Distribution definition=“http://uncertml.org/gaussian”> <parameter definition=“http://uncertml.org/mean”>34.2</parameter> <parameter definition=“http://uncertml.org/variance”>12.4</parameter></Distribution>
<GaussianDistribution> <mean>34.2</mean> <variance>12.4</variance></GaussianDistribution>
THE UNCERTML DICTIONARY
Weak-typed designs rely on dictionaries.
Includes definitions of key distributions & statistics.
URIs link to dictionary entry and provide semantics.
Could be written in Semantic Web standards (OWL, RDF etc).
<gml:Dictionary xmlns:gml="http://www.opengis.net/gml" gml:id="DISTRIBUTIONS"> <gml:name>All Probability Distributions</gml:name> <gml:description>Distributions dictionary</gml:description> <gml:dictionaryEntry> <un:DistributionDefinition xmlns:un="http://www.intamap.org/uncertml" gml:id="Gaussian"> <gml:description>Gaussian distribution</gml:description> <gml:name>Gaussian</gml:name> <gml:name>Normal</gml:name> <un:functions> <un:FunctionDefinition gml:id="Gaussian_Cumulative_Distribution_Function"> <gml:description>cumulative distribution function</gml:description> <gml:name>Cumulative Distribution Function</gml:name> <un:mathML> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac>
UNCERTML – DICTIONARY EXAMPLE
SEPARATION OF CONCERNS
Several competing standards already exist addressing the issue of units and location.
Geospatial information not always relevant – Systems biology.
Do what we know – do it well!
UNCERTMLAn applied case study
THE INTAMAP PROJECT
An automatic, interoperable service providing real time interpolation between observations.
EURDEP providing radiological data as a case study.
Provide real time predictions to aid risk management through a Web Processing Service interface.
UNCERTML IN INTAMAP ‘Really clever’ Bayesian
inference: Different sensor errors. Change of support.
Fast & approximate algorithms.
COMPARING PREDICTIONS WITH AND WITHOUT UNCERTML
Without UncertML With UncertML
CONCLUSIONSCurrently no interoperable standard
which fully describes random variables.
UncertML provides an extensible, weak-typed, design that can quantify uncertainty using:Distributions.Statistics.Realisations.
Provide richer information for use in decision support systems.
UNCERTML IN INTAMAP<om:Observation><om:procedure xlink:href="http://www.mydomain.com/sensor_models/temperature"/> <om:resultQuality> <un:Distribution definition="http://dictionary.uncertml.org/distributions/gaussian"> <un:parameters> <un:Parameter definition="http://dictionary.uncertml.org/distributions/gaussian/parameters/mean"> <un:value>0.0</un:value> </un:Parameter> <un:Parameter definition="http://dictionary.uncertml.org/distributions/gaussian/parameters/variance"> <un:value>3.6</un:value> </un:Parameter> </un:parameters> </un:Distribution> </om:resultQuality> <om:observedProperty xlink:href="urn:x-ogc:def:phenomenon:OGC:AirTemperature"/> <om:featureOfInterest> <sa:SamplingPoint> <sa:sampledFeature xlink:href="http://www.mydomain.com/sampling_stations/ws-04231"/> <sa:position> <gml:Point> <gml:pos srsName="urn:x-ogc:def:crs:EPSG:4326"> 52.4773635864 -1.89538836479 </gml:pos> </gml:Point> </sa:position> </sa:SamplingPoint> </om:featureOfInterest> <om:result xsi:type="gml:MeasureType" uom="urn:ogc:def:uom:OGC:degC">19.4</om:result></om:Observation>
<un:DistributionArray> <un:elementType> <un:Distribution definition="http://dictionary.uncertml.org/distributions/gaussian"> <un:parameters> <un:Parameter definition="http://dictionary.uncertml.org/distributions/gaussian/mean"/> <un:Parameter definition="http://dictionary.uncertml.org/distributions/gaussian/variance"/> </un:parameters> </un:Distribution> </un:elementType> <un:elementCount>5</un:elementCount> <swe:encoding> <swe:TextBlock decimalSeparator="." blockSeparator=" " tokenSeparator=","/> </swe:encoding> <swe:values> 35.2,56.75 31.2,65.31 28.2,54.23 35.6,45.21 41.5,85.24 </swe:values></un:DistributionArray>