UNCERTML - DESCRIBING AND COMMUNICATING UNCERTAINTY WITHIN THE (SEMANTIC) WEB

looneyvillebiologistInternet και Εφαρμογές Web

21 Οκτ 2013 (πριν από 3 χρόνια και 10 μήνες)

65 εμφανίσεις

UNCERTML
-

DESCRIBING
AND COMMUNICATING
UNCERTAINTY WITHIN
THE (SEMANTIC) WEB


Matthew Williams

williamw@aston.ac.uk

OVERVIEW


Introduction.



Motivation


the Semantic and Sensor
Webs.



UncertML overview & design choices.



Use case


The INTAMAP project.



Conclusions.


MOTIVATION

The semantic and sensor webs

THE SEMANTIC WEB


Most Web content today is designed for humans
to read, not computers.



Semantic Web will bring structure to the
meaningful content of Web pages.



Adding logic to the Web allows rules to be used
for inference.



Ontologies are used to describe entities and
relations between entities.

HOW UNCERTAINTY IS USED
WITHIN THE SEMANTIC WEB


PW
-
OWL: a Bayesian Ontology Language for the
Semantic Web:


Extends OWL to allow probabilistic knowledge to be
represented in an ontology.


U
s
ed for reasoning with Bayesian inference.


Random variables are described by either a PR
-
OWL
table (discrete probability) or using a proprietary
format


NOT freely available.


Other standards looking at similar concepts:


BayesOWL.


FuzzyOWL.

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
>

WHAT IS MISSING?


A formal open standard for quantifying complex
uncertainties:


Distributions.


Statistics.


Realisations.



UNCERTML

I’ve done it!!

OVERVIEW


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


STATISTICS

<
un:Statistic

definition
="
http://dictionary.uncertml.org/statistics/standard_
deviation
">


<
un:value
>
12.08
</
un:value
>

</
un:Statistic
>


DISTRIBUTIONS

<
un:Distribution

definition
="
http://dictionary.uncertml.org/distributions/gauss
ian
">


<
un:parameters
>


<
un:Parameter

definition
="
http://dictionary.uncertml.org/distributions/gauss
ian/mean
">


<
un:value
>
34.564
</
un:value
>


</
un:Parameter
>


<
un:Parameter

definition
="
http://dictionary.uncertml.org/distributions/gauss
ian/variance
">


<
un:value
>
67.45
</
un:value
>


</
un:Parameter
>


</
un:parameters
>

</
un:Distribution
>


REALISATIONS

<
un:Realisations

definition
="
http://dictionary.uncertml.org/realisation
"

samplingMethod
="
http://dictionary.uncertml.org/realisations/sampling_
methods/MCMC
"

realisedFrom
="
http://dictionary.uncertml.org/distributions/gaussian
">


<
un:realisationsCount
>
100
</
un:realisationsCount
>


<
un:elementCount
>
100
</
un:elementCount
>


<
swe:encoding
>


<
swe:TextBlock

decimalSeparator
="
.
"

blockSeparator
="

"

tokenSeparator
="
,
"/>


</
swe:encoding
>


<
swe:values
>


<!
--

[100 space separated values]
--
>


</
swe:values
>

</
un:Realisations
>


UNCERTML

Difficult decisions and design principles

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

<Feature type="Road">


<property name="description" type="string">...</property>


<property name="surfaceTreatment" type="token">Bitumen</property>

</Feature>

<Road>


<description>...</description>


<surfaceTreatment>Bitumen</surfaceTreatment>

</Road>

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).

UNCERTML


DICTIONARY
EXAMPLE

<
gml:Dictionary

xmlns:gml
="
http://www.opengis.net/gml
"

gml:id
="
DISTRIBUTIONS
">


<
gml:name
>
All Probability Distributions
</
gml:name
>


<
gml:description
>
This is a dictionary...
</
gml:description
>


<
gml:dictionaryEntry
>


<
un:DistributionDefinition

xmlns:un
="
http://www.intamap.org/uncertml
"

gml:id
="
Gaussian
">


<
gml:description
>
This is a 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
>
This is a 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
>

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!

UNCERTML WITHIN THE SEMANTIC
WEB


Proprietary software can impede interoperability
which is detrimental to the Semantic Web.



Discrete probability tables can only provide so
much information.



Provide an open standard for describing the
complex probability distributions that are
currently lacking within PR
-
OWL.


UNCERTML WITHIN THE SENSOR
WEB


resultQuality of an O&M Observation.


Encode sensor bias and other inherent uncertainties
of a sensor observation.



Q
uality property of SWE types.


Effectively provides a ‘Random Variable’ type.



Positional uncertainty within GML.


Extending GML would allow UncertML to integrate
with the geometry types to provide positional
uncertainty information.

UNCERTML

Does it actually work??

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

<
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
>


‘Really clever’ Bayesian
inference:


Different sensor errors.


Change of support.


Fast & approximate
algorithms.

COMPARING PREDICTIONS WITH
AND WITHOUT UNCERTML

Without UncertML

With UncertML

CONCLUSIONS


Currently no existing standard to
describe uncertainty within the
Semantic and Sensor Webs.


UncertML provides an extensible,
weak
-
typed, design that can quantify
uncertainty using:


Distributions.


Statistics.


Realisations.


P
r
ovide more information for use in
decision support systems


especially
useful in risk management.