AGU Fall Meeting, 2015-12-16 A number of models for observation metadata have been developed in the earth and environmental science communities, including OGC’s Observations and Measurements (O&M), the ecosystems community’s Extensible Observation Ontology (OBOE), the W3C’s Semantic Sensor Network Ontology (SSNO), and the CUAHSI/NSF Observations Data Model v2 (ODM2). In order to combine data formalized in the various models, mappings between these must be developed. In some cases this is straightforward: since ODM2 took O&M as its starting point, their terminology is almost completely aligned. In the eco-informatics world observations are almost never made in isolation of other observations, so OBOE pays particular attention to groupings, with multiple atomic ‘Measurements’ in each oboe:Observation which does not have a result of its own and thus plays a different role to an om:Observation. And while SSN also adopted terminology from O&M, mapping is confounded by the fact that SSNO uses DOLCE as its foundation and places ssn:Observations as ‘Social Objects’ which are explicitly disjoint from ‘Events’, while O&M is formalized as part of the ISO/TC 211 harmonised (UML) model and sees om:Observations as value assignment activities. Foundational ontologies (such as BFO, GFO, UFO or DOLCE) can provide a framework for alignment, but different upper ontologies can be based in profoundly different world-views and use of incommensurate frameworks can confound rather than help. A potential resolution is provided by comparing recent studies that align SSNO and O&M, respectively, with the PROV ontology. PROV provides just three base classes: Entity, Activity and Agent. om:Observation is sub-classed from prov:Activity, while ssn:Observation is sub-classed from prov:Entity. This confirms that, despite the same name, om:Observation and ssn:Observation denote different aspects of the observation process: the observation event, and the record of the observation event, respectively. Alignment with the simple PROV classes has clarified this issue in a way that had previously proved difficult to resolve. The simple 3-class base model from PROV appears to provide just enough logic to serve as a lightweight upper ontology, particularly for workflow or process-based information.

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 simon.cox@csiro.au
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