Download - Pitfalls in alignment of observation models resolved using PROV as an upper ontology

Pitfalls in alignment of observation models resolved using PROV as an upper ontology Simon Cox | Research Scientist | Environmental Informatics16 December2015

LAND AND WATER

https://creativecommons.org/licenses/by/4.0

Simon Cox - AGU Fall Meeting 2015 - IN33F-07

Overlapping terminology

Sources: OGC SensorMLOGC Observations and Measurements (O&M) ISO General Feature Model

Semantic Sensor Network Ontology (SSN) DOLCE UltraLite

Biological Collections Ontology (BCO) Basic Formal Ontology

Contentious terms:ObservationProcess


SensorML - Process

All components modeled as processes, including

• Hardware - transducers, sensors, platforms

• Software

Botts & Robin, OGC SensorML – OGC Implementation Specification OGC document 07-000, 12-000

O&M – Process, Observation

OM_Observation

+ phenomenonTime+ resultTime+ validTime [0..1]+ resultQuality [0..*]+ parameter [0..*]

GF_PropertyType

GFI_Feature

OM_Process Any

+observedProperty

1

0..*

+featureOfInterest 1

0..*

+procedure1 +result

An Observation is an action whose result is an estimate of the value of some property of the feature-of-interest, obtained using a specified procedure

Simon Cox - AGU Fall Meeting 2015 - IN33F-07 Cox, OGC Abstract Specification – Topic 20: Observations and Measurements 2.0 ISO 19156:2011 Geographic Information – Observations and measurements

‘Observation’ produces result at a known time

Before resultTime: no dataAfter resultTime: data available

‘Process’ is reusable observation procedure


om-lite <http://def.seegrid.csiro.au/ontology/om/om-lite>

S.J.D. Cox, Ontology for observations and sampling features, with alignments to existing models, Semant. Web J. (2015) Accepted http://www.semantic-web-journal.net/content/ontology-observations-and-sampling-features-alignments-existing-models-0


SSN – Process, Observation• Observation, Process both ‘Social Objects’• Stimulus is the only ‘Event’

M. Compton, P. Barnaghi, L. Bermudez, R. García-Castro, O. Corcho, S.J.D. Cox, et al., The SSN ontology of the W3C semantic sensor network incubator group, Web Semant. Sci. Serv. Agents World Wide Web. 17 (2012) 25–32. doi:10.1016/j.websem.2012.05.003.

Simon Cox - AGU Fall Meeting 2015 - IN33F-07 Walls RL, Deck J, Guralnick R, Baskauf S, Beaman R, et al. (2014) Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies. PLoS ONE 9(3): e89606. doi:10.1371/journal.pone.0089606

BCO - ObservingProcess ObservingProcess subClassOf* BFO:Occurrent


Process-flow model

Core PROV

Developed primarily for datasets, data products, reports

T. Lebo, S. Sahoo, D.L. McGuinness, PROV-O: The PROV Ontology, (2013). http://www.w3.org/TR/prov-o/ (accessed February 13, 2014).


Core PROV– aligned with BFO/BCO

bfo:Occurrent??

bfo:Continuant

bco:ObservingProcess


Core PROV– alignment with O&M

om:Observationom:Process

om:Result


Core PROV– alignment with SSN

??ssn:Sensor

ssn:Observation


SSNX aligned with PROV

M. Compton, D. Corsar, K. Taylor, Sensor Data Provenance: SSNO and PROV-O Together at Last, in: 7th Int. Work. Semant. Sens. Networks, 2014.


Core PROV– alignment with SSNX

ssnx:ActivityOfSensingssn:Sensor

ssn:ObservationRelates to sensor as an asset?


bfo:Continuant

Core PROV– all alignments

ssnx:ActivityOfSensing

ssn:Sensor

ssn:Observation

bfo:Occurrent

bco:ObservingProcess

om:Observation

om:Process

Generation of observation data matches a generic process model PROV is a convenient upper-ontology for alignments

Reusable agents


Sampling Features - sam-lite ontology

S.J.D. Cox, Ontology for observations and sampling features, with alignments to existing models, Semant. Web J. (2015) Accepted http://www.semantic-web-journal.net/content/ontology-observations-and-sampling-features-alignments-existing-models-0


Core PROV– alignment with Specimen prep

sam:Process

sam:Specimen

sam:PreparationStep


Specimen preparation and observation traceLifecycle events modelled as prov:Activity instances• Analysis• Sieving• Grinding• Splitting• Specimen retrieval

People and machines modelled as prov:Agent instances• Lab Tech, Geologist• Sieve stack• Mill• Saw• Hammer

Cox, SJD & Car, NJ Provenance of things - describing geochemistry observation workflows using PROV-O, IN33A-1784


Other alignments and extensions

prov:Entity ← :PhysicalEntity ← :Specimen prov:Entity ← prov:Plan ← :SamplingProtocol

prov:Agent ← :SampleProcessingSystem ← :GrindingSystem, :PolishingSystem, :DissolvingSystem, :FusingSystemprov:Agent ← :SampleRetrievalSystem ← :FieldSamplingSystemprov:Agent ← :SubSamplingSystem ← :BiasedSplittingSystem ← :SizeSeparationSystem , :DensitySeparationSystem, :MagneticSeparationSystemprov:Agent ← Instrument , Sensorprov:wasAssociatedWith ← :wasControlledBy, :wasSponsoredBy, :wasRequestedByprov:wasDerivedFrom ← :unbiasedSplitFrom, :biasedSplitFromprov:wasDerivedFrom ← prov:hadPrimarySource ← :fieldSpecimen


Summary - in praise of PROV • Observation models/ontologies use terms “observation” and “process”

• Inter-community discussions are vulnerable to misunderstandings

• Grounding in traditional ‘upper ontologies’ doesn’t necessarily help!

• Generating results of observations is essentially a process-chain PROV provides a lightweight ‘upper ontology’ that can help

LAND AND WATER

Thank youCSIRO Land and WaterSimon CoxResearch Scientistt +61 3 9252 6342e [email protected] www.csiro.au/people/simon.cox


OBOE observation model

One Observation is composed of multiple Measurements

Each for a different Characteristic of the same Entity


OBOE observation model


om:ObservationCollection oboe:Observationcommon feature-of-interest, phenomenonTime

om:Observation oboe:Measurement feature-of-interest, phenomenonTime from collection

Download - Pitfalls in alignment of observation models resolved using PROV as an upper ontology

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