combining process and sensor ontologies to support geo-sensor data retrieval
TRANSCRIPT
Anusuriya Devaraju1, Holger Neuhaus2, Krzysztof Janowicz3, Michael Compton41University of Muenster | anusuriya.devaraju@uni‐muenster.de
2Tasmanian ICT Centre, CSIRO |[email protected]
GIScience 2010 ‐ 6th International Conference on Geographic Information Science, 14‐17th September 2010.
Tasmanian ICT Centre, CSIRO |[email protected] Pennsylvania State University | [email protected]
4 CSIRO ICT Centre, Canberra | [email protected]
Table of Contents
1. Background & Motivation2. Ontologies2. Ontologies
Sensor Network Ontology (SNO)
Process‐centric Hydrology Domain Ontology (HDO)
3. Use case : Lake Evaporation4. Discussion and Conclusions
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Background
Sensor Web allows access to an avalanche of environmental dataNevertheless, an effort is required to collate and interpret themNevertheless, an effort is required to collate and interpret them– e.g., Incompatible schemas classification & naming conflicts
Observation Archives
DPIPWEStreamFlow
XML
Current stream flow data along river X?
WDS
HydroTasWaterCourseDischarge
XML
river X?
Sensor Collection Service
SWE Client
StreamDischarge
XML
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Sensor Collection Service
The ChallengeExisting ontological approaches are sensor‐observation focused– Jurdak et al. (2004), Bermudez et al.(2006), Russomanno et al. (2005),
T i thi&B b i (2008) L P lli (2007) B bit ki t l (2009) K hTripathi&Babaie (2008), Lopez‐Pellicer (2007), Babitski et al. (2009), Kuhn (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 precipitation as input for runoff models
Sensor & Sensing Procedure, Physical Property, Locationprecipitation as input for runoff models Property, Location
The amount of water available for runoff in a catchment (e.g., snowmelt, rainfall)
Physical Property, Feature, Occurrence Types & Temporal Property, Location
Duration of significant precipitation Occurrence Types &Temporal Property
4* http://www.weather.gov/oh/docs/alfws‐handbook/appB.pdf
Duration of significant precipitation Occurrence Types &Temporal Property, Location
Our Approach
Involves representation of sensing procedures, observed properties and geographic entitiesp p g g pA combined approach which relates a sensor network ontology to a process‐centric domain ontology
Semantic‐based Sensor –
Observation Discovery and
RetrievalRetrieval
Sensing procedure , devices, observation
Observed domain (feature of interest, physical property)
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Sensor Network Ontology (SNO)
Largely compatible with SensorML and O&M specificationsDistinguishes between sensing procedure and sensing devicesprocedure 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
[The partial view of the Sensor Network Ontology (SNO)*]
sensors can be represented in terms of their operations
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[The partial view of the Sensor Network Ontology (SNO) ]
* http://www.w3.org/2005/Incubator/ssn/wiki/images/4/42/SensorOntology20090320.owl.xml
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 ApproachOntological Approach(A DOLCE‐aligned surface hydrology domain ontology)
Geo ProcessesObserved Properties Geo‐ProcessesObserved Properties
7* The notion ‘geo‐processes’ is used here rather broadly as it includes all kinds of dynamic entities, e.g., process, event
A Glimpse of Domain Ontology (HDO)
Categories describing evaporation and transpiration conceptsRelated 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
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 evaporationa Point measurementsa. Point measurements
performed by an instrument (e.g., evaporation pan)evaporation pan)
9* Key component in the Hydrological Sensor Web research by the CSIRO Water for a Healthy Country Flagship initiative.
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 evaporationb Calculation using otherb. Calculation using other
measured meteorological variablesvariables
8 * Key component in the Hydrological Sensor Web research by the CSIRO Water for a Healthy Country Flagship initiative.
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 descriptions and sensing domain (features & physical properties)p g ( p y p p )
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
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Discussion & ConclusionsProcess‐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*
l• Synonymous properties – e.g., EvaporationRate & Actual Evaporation
– 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
l h d fi h bili i f d k• Ontology that defines the capabilities of sensors and sensor networks
– Domain ontology improvement• Refines the descriptions of occurrence types
• Specifies participants based on their role with respect to an occurence
10 * http://www.bom.gov.au/climate/cdo/about/definitionsother.shtml
DankeDanke
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