Pitfalls in alignment of observation models resolved using PROV as an upper ontology
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The document discusses the alignment of observation models using the Provenance Ontology (PROV) as an upper ontology, particularly in the context of environmental data. It highlights the complexities in terminology related to observations and processes and emphasizes the significance of PROV in facilitating clearer communication across various communities. Additionally, it addresses the reusable nature of observation processes and the alignment of different ontologies to support data provenance in scientific workflows.
![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 data
After resultTime: data available
‘Process’ is reusable observation
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Pitfalls in alignment of observation models resolved using PROV as an upper ontology
- 1. Pitfalls in alignment of observation models resolved using PROV as an upper ontology Simon Cox | Research Scientist | Environmental Informatics 16 December2015 LAND AND WATER
- 2. Overlapping terminology Sources: OGC SensorML OGC 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: Observation Process Simon Cox - AGU Fall Meeting 2015 - IN33F-07
- 3. SensorML - Process Simon Cox - AGU Fall Meeting 2015 - IN33F-07 All components modeled as processes, including • Hardware - transducers, sensors, platforms • Software Botts & Robin, OGC SensorML – OGC Implementation Specification OGC document 07-000, 12-000
- 4. 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 data After resultTime: data available ‘Process’ is reusable observation procedure
- 5. om-lite <http://def.seegrid.csiro.au/ontology/om/om-lite> Simon Cox - AGU Fall Meeting 2015 - IN33F-07 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
- 6. SSN – Process, Observation Simon Cox - AGU Fall Meeting 2015 - IN33F-07 • 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.
- 7. 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 Simon Cox - AGU Fall Meeting 2015 - IN33F-07
- 8. Process-flow model Core PROV Simon Cox - AGU Fall Meeting 2015 - IN33F-07 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).
- 9. Core PROV– aligned with BFO/BCO Simon Cox - AGU Fall Meeting 2015 - IN33F-07 bfo:Occurrent ?? bfo:Continuant bco:ObservingProcess
- 10. Core PROV– alignment with O&M Simon Cox - AGU Fall Meeting 2015 - IN33F-07 om:Observation om:Process om:Result
- 11. Core PROV– alignment with SSN Simon Cox - AGU Fall Meeting 2015 - IN33F-07 ?? ssn:Sensor ssn:Observation
- 12. SSNX aligned with PROV Simon Cox - AGU Fall Meeting 2015 - IN33F-07 M. Compton, D. Corsar, K. Taylor, Sensor Data Provenance: SSNO and PROV-O Together at Last, in: 7th Int. Work. Semant. Sens. Networks, 2014.
- 13. Core PROV– alignment with SSNX Simon Cox - AGU Fall Meeting 2015 - IN33F-07 ssnx:ActivityOfSensing ssn:Sensor ssn:Observation Relates to sensor as an asset?
- 14. bfo:Continuant Core PROV– all alignments Simon Cox - AGU Fall Meeting 2015 - IN33F-07 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
- 15. Sampling Features - sam-lite ontology Simon Cox - AGU Fall Meeting 2015 - IN33F-07 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
- 16. Core PROV– alignment with Specimen prep Simon Cox - AGU Fall Meeting 2015 - IN33F-07 sam:Process sam:Specimen sam:PreparationStep
- 17. Specimen preparation and observation trace Lifecycle 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 Simon Cox - AGU Fall Meeting 2015 - IN33F-07 Cox, SJD & Car, NJ Provenance of things - describing geochemistry observation workflows using PROV-O, IN33A-1784
- 18. Other alignments and extensions prov:Entity ← :PhysicalEntity ← :Specimen prov:Entity ← prov:Plan ← :SamplingProtocol prov:Agent ← :SampleProcessingSystem ← :GrindingSystem, :PolishingSystem, :DissolvingSystem, :FusingSystem prov:Agent ← :SampleRetrievalSystem ← :FieldSamplingSystem prov:Agent ← :SubSamplingSystem ← :BiasedSplittingSystem ← :SizeSeparationSystem , :DensitySeparationSystem, :MagneticSeparationSystem prov:Agent ← Instrument , Sensor prov:wasAssociatedWith ← :wasControlledBy, :wasSponsoredBy, :wasRequestedBy prov:wasDerivedFrom ← :unbiasedSplitFrom, :biasedSplitFrom prov:wasDerivedFrom ← prov:hadPrimarySource ← :fieldSpecimen Simon Cox - AGU Fall Meeting 2015 - IN33F-07
- 19. 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 Simon Cox - AGU Fall Meeting 2015 - IN33F-07
- 20. LAND AND WATER Thank youCSIRO Land and Water Simon Cox Research Scientist t +61 3 9252 6342 e [email protected] w www.csiro.au/people/simon.cox
- 21. OBOE observation model Simon Cox - AGU Fall Meeting 2015 - IN33F-07 One Observation is composed of multiple Measurements Each for a different Characteristic of the same Entity
- 22. OBOE observation model Simon Cox - AGU Fall Meeting 2015 - IN33F-07
- 23. Simon Cox - AGU Fall Meeting 2015 - IN33F-07 om:ObservationCollection oboe:Observation common feature-of-interest, phenomenonTime om:Observation oboe:Measurement feature-of-interest, phenomenonTime from collection