Your SlideShare is downloading. ×
Combining Process and Sensor Ontologies to Support Geo-Sensor Data Retrieval
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Combining Process and Sensor Ontologies to Support Geo-Sensor Data Retrieval

967
views

Published on

Published in: Education

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
967
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
20
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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