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UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY

UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY. Matthew Williams williamw@aston.ac.uk. 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.

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UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY

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  1. UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY Matthew Williams williamw@aston.ac.uk

  2. OVERVIEW • Introduction. • Motivation – the Semantic and Sensor Webs. • UncertML overview. • Use case – The INTAMAP project. • Conclusions.

  3. MOTIVATION The semantic and sensor webs

  4. THE SENSOR WEB

  5. 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 formalstandard 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>

  6. 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.

  7. What next? • A formal open standard for quantifying complex uncertainties • Extend to allow continuous distributions • More powerful reasoning, richer representations

  8. UNCERTML

  9. OVERVIEW • Split into three distinct packages (distributions, statistics & realisations).

  10. 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>

  11. UNCERTML An overview

  12. WEAK VS. STRONG Weak-typed Strong-typed • 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 <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>

  13. 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).

  14. UNCERTML – DICTIONARY EXAMPLE <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>

  15. 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!

  16. UNCERTML An applied case study

  17. 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.

  18. UNCERTML IN INTAMAP • ‘Really clever’ Bayesian inference: • Different sensor errors. • Change of support. • Fast & approximate algorithms.

  19. COMPARING PREDICTIONS WITH AND WITHOUT UNCERTML Without UncertML With UncertML

  20. CONCLUSIONS • Currently 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.

  21. 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>

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