1. Anusuriya Devaraju1, Holger Neuhaus2, Krzysztof Janowicz3, Michael Compton4
1University of Muenster | anusuriya.devaraju@uni‐muenster.de
2Tasmanian ICT Centre, CSIRO |holger.neuhaus@csiroalumni.org.au
Tasmanian ICT Centre, CSIRO |holger.neuhaus@csiroalumni.org.au
3 Pennsylvania State University | jano@psu.edu
4 CSIRO ICT Centre, Canberra | michael.compton@csiro.edu
GIScience 2010 ‐ 6th International Conference on Geographic Information Science, 14‐17th September 2010.

2. Table of Contents
1. Background & Motivation
2. Ontologies
Sensor Network Ontology (SNO)
Process‐centric Hydrology Domain Ontology (HDO)
3. Use case : Lake Evaporation
4. Discussion and Conclusions
2

3. Background
Sensor Web allows access to an avalanche of environmental data
Nevertheless, an effort is required to collate and interpret them
Nevertheless, an effort is required to collate and interpret them
– e.g., Incompatible schemas classification & naming conflicts
Observation Archives
Stream
DPIPWE Flow Current
XML stream flow
data along
river X?
river X?
HydroTas
WaterCourse
WDS Discharge
XML
Stream
Discharge
XML
SWE Client
Sensor Collection Service
Sensor Collection Service
3

4. The Challenge
Existing ontological approaches are sensor‐observation focused
– Jurdak et al. (2004), Bermudez et al.(2006), Russomanno et al. (2005),
Tripathi&Babaie (2008), Lopez‐Pellicer (2007), Babitski et al. (2009), Kuhn
T i thi&B b i (2008) L P lli (2007) B bit ki t l (2009) K h
(2009), Janowicz et al. (2010) and more...
– in some cases, the relations to real world entities are missing..
However, sensor and observation queries are often expressed in
terms of sensors, observations and features. Consider the
following example* :
g p
Requirements Query Elements
Techniques used for estimating Sensor & Sensing Procedure, Physical
precipitation as input for runoff models
precipitation as input for runoff models Property, Location
Property, Location
The amount of water available for runoff Physical Property, Feature, Occurrence
in a catchment (e.g., snowmelt, rainfall) Types & Temporal Property, Location
Duration of significant precipitation
Duration of significant precipitation Occurrence Types &Temporal Property,
Occurrence Types &Temporal Property
Location
* http://www.weather.gov/oh/docs/alfws‐handbook/appB.pdf 4

5. Our Approach
Involves representation of sensing procedures, observed
p p
properties and geographic entities
g g p
A combined approach which relates a sensor network ontology to
a process‐centric domain ontology
Semantic‐based
Sensor –
Observation
Discovery and
Retrieval
Retrieval
Sensing procedure , Observed domain (feature of
devices, observation interest, physical property)
5

6. Sensor Network Ontology (SNO)
Largely compatible with
SensorML and O&M
specifications
Distinguishes between sensing
procedure and sensing devices
procedure and sensing devices
– Sensor is not limited to instruments
– Procedure describes how the
sensor makes an observation
Simple as well multi‐component
sensors can be represented in
sensors can be represented in
terms of their operations
[The partial view of the Sensor Network Ontology (SNO) ]
[The partial view of the Sensor Network Ontology (SNO)*]
* http://www.w3.org/2005/Incubator/ssn/wiki/images/4/42/SensorOntology20090320.owl.xml 6

7. Process‐centric Domain Ontology (HDO)
The aim is to relate the observed properties to geo‐processes*
In a bigger context, observation interpretation involves understanding
geo‐processes in which the bearers of the observed properties participate.
Describes domain of sensing (features of interest and physical properties)
Process‐Centric
Ontological Approach
Ontological Approach
(A DOLCE‐aligned
surface hydrology
domain ontology)
Observed Properties
Observed Properties Geo‐Processes
Geo Processes
* The notion ‘geo‐processes’ is used here rather broadly as it includes all kinds of dynamic entities, e.g., process, event 7

8. A Glimpse of Domain Ontology (HDO)
Categories describing evaporation and transpiration concepts
Related via basic ontological relations from DOLCE : subsumption, parthood,
constitution, participation, inherence, etc.
Properties are classified based on units relevant to hydrology in SI
measurement
[The partial view of ET‐ related categories*]
* http://ifgi.uni‐muenster.de/~a_deva01/publication.html 8

9. Use Case Scenario (Lake Evaporation)
The Sensor Ontology (SNO) leaves the observed domain
unspecified; the domain categories are supplied by our surface
hydrology ontology (HDO)
Methods for estimating lake evaporation
a.
a Point measurements
Point measurements
performed by an
instrument (e.g.,
evaporation pan)
evaporation pan)
* Key component in the Hydrological Sensor Web research by the CSIRO Water for a Healthy Country Flagship initiative. 9

10. Use Case Scenario (Lake Evaporation)
The Sensor Ontology (SNO) leaves the observed domain
unspecified; the domain categories are supplied by our surface
hydrology ontology (HDO)
Methods for estimating lake evaporation
b.
b Calculation using other
Calculation using other
measured
meteorological
variables
* Key component in the Hydrological Sensor Web research by the CSIRO Water for a Healthy Country Flagship initiative. 8

11. Discussion & Conclusions
Our approach presents an ‘integrated view’ of the Semantic
Sensor Web, in addition to a sensor‐observation centric
,
approach.
Combining sensor concepts with domain concepts
– Helps evaluate the design of both ontologies
– Supports observation request involving interplay between sensor
p g ( p y p p )
descriptions and sensing domain (features & physical properties)
Sensor Network Ontology (SNO)
– A particular sensor can be described at multiple levels of abstraction; this
promotes discovery and reusability of sensor.
• e.g., In the absence of a measured evaporation rate, this property can be
estimated from the meteorological variables
9

12. Discussion & Conclusions
Process‐centric Domain Ontology (HDO)
– Specifies the relations between geo‐processes, participants and properties
– Handles naming heterogeneities.
• Process distinction – e.g., Evapotranspiration is sometimes used
interchangeably with Evaporation*
• Synonymous properties – e.g., EvaporationRate & Actual Evaporation
l
– Allows a more complex observation request
• e.g., waterloss from a catchment within a given period.
Ongoing work
– SNO & W3C Semantic Sensor Network Incubator Group
• Ontology that defines the capabilities of sensors and sensor networks
l h d fi h bili i f d k
– Domain ontology improvement
• Refines the descriptions of occurrence types
• Specifies participants based on their role with respect to an occurence
* http://www.bom.gov.au/climate/cdo/about/definitionsother.shtml 10

13. Danke
13