1. A Process-Centric
Ontological Approach for
Integrating
teg at g
Geo-Sensor Data
Anusuriya Devaraju & Werner Kuhn
Institute For Geoinformatics,
University of Muenster
{anusuriya.devaraju, kuhn}@uni‐muenster.de
{ }
FOIS 2010 ‐ 6th International Conference on Formal Ontology in Information Systems, 13th May 2010.
2. A Simple Example…
Mrs Schneider:
cut thin pieces Mr Schneider:
from a large cut into or shape
piece of cooked (a hard material)
meat to produce an
object or design
Image Source : http://www.cartoonstock.com/directory/C/Carving.asp 2
4. Background
Geo‐sensors provide key
information about geo‐processes
g p
One way to interpret sensor
observations is by looking at geo‐
processes that influence them.
processes that influence them
Challenge :
– How to relate observed properties to
geo‐processes?
– Develop an approach that captures
consensual knowledge of the surface
hydrology domain
hydrology domain
(Observed Properties and Hydro‐Processes)
4
5. Motivation
Lack of principled ways of describing different kinds of
occurrences
– In GI domain, the terminological inconsistencies have led to
h l l h l
disagreements on classifying processes and events [Galton, 2008]
– Existing work : [Yuan, 2001], [Dias, 2004], [Wang, 2004], [Worboys, 2005],
etc.
etc
Are observed properties sufficient to classify or identify geo‐
processes?
– Objects & Matter as the ‘bearer’ of observed properties.
Handle semantic heterogeneities within geo‐sensor data
– H dl diff
Handle differences in naming conventions for (a) observed properties
i i i f ( ) b d i
(e.g., Gauge Height | Raw Stage) and (b) geo‐processes (e.g., InterFlow |
SubsurfaceStormFlow)
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6. Motivation
From Sensor Web Community
– An ontology of observable property‐types to improve the discovery and
retrieval of sensor data sources must be available [SWE, OGC 2007].
– Eventually, the integration of domain ontologies [……..], semantic queries
and semantic transformations in Sensor Web infrastructure have to be
addressed [GEOSS Sensor Web Workshop Report, 2008]. It is necessary to
have sensor ontology to specify sensor capabilities in sensor ontology, as
well as the observed phenomena and complementing domain ontology to
specify what is being measured and the relation between the observed
if h i b i d d h l i b h b d
properties and features of interest in domain.
Existing approach to support semantic integration of geo‐sensor
data
– Focused on ontologies for sensors, observed properties, entities (e.g.,
p y
physical object). More examples in the paper ☺
j ) p p p
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7. Approach
Process‐Centric*
Ontological Approach
(A DOLCE‐aligned
surface hydrology
ontology)
Observed Properties Geo‐Processes
DOLCE specifies (i) a basic level distinction between processes and events and
(ii) relations between processes and physical properties (via participants)
Related work based on DOLCE
Observation & Sensor [Probst (2007), Kuhn & Ortmann(2010); Neuhaus & Compton
(2005), Babitski et al. (2009); Fallahi (2008) ; [Brodaric & Probst (2009)]; Extreme‐
Events [Sherp et al. (2009), MONITOR]……
Events [Sherp et al (2009) MONITOR]
*The notion ’process’ encompasses different kinds of perdurants like process & event 7
9. Ontological Relations
Relation Example
Subsume
S b All individuals of a universal are necessarily individuals of another
All i di id l f i l il i di id l f th
SB(WaterObject , Lake) ; SB(PrecipitationProcess ,
SnowProcess)
Participation Relates endurants to perdurants in which they participate.
Relates endurants to perdurants in which they participate.
PC(Vegetation(x),TranspirationProcess(y),T(t))
Parthood A time‐independent relation holding between two individuals of
perdurants or abstracts.
PP(SnowflakeMelting(x),RainProcess(y))
Temporary A relation between two individuals of endurant where one is part of the
Parthood other at a particular time.
P(Headwater(x),River(y),T(t))
P(Headwater(x) River(y) T(t))
Inherence A relation between an individual quality and its bearer.
qt(Salinity(x), River(y))
qt(PrecipitationDuration(a),PrecipitationProcess(b))
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11. Discussions: Sensor Data Retrieval
Importing domain categories into
the sensor network ontology.
Resolving naming ambiguity
o One process can be distinguished
from other processes via the
o o e p ocesses a e
participation relation
o equivalentClass relation identifies a
synonymous category Sensor Network Ontology (Neuhaus, 2009)
Improving sensor‐data retrieval
o Observation requests based on the relations between processes, their
pa t c pa ts as e as t e p ope t es
participants as well as their properties.
o Example : “How long did the rainstorm occurred in a given watershed during the
above period? (Asking information about duration) How much water was received
from the specified storm? (Asking information about interaction)”. What is the number
of days since last precipitation? (dry period preceding precipitation)
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12. Ongoing & Future Work
It is harder to pinpoint the bearer of a quality
– The definition of ‘features’ (from OGC s O&M specification) allows any
The definition of features (from OGC’s O&M specification) allows any
‘entity’ to be classified as a feature type (e.g., geographic objects, event)
– In DOLCE, a physical quality only inhere‐in a physical endurant!
Further investigations are required on the concept quality
F th i ti ti i d th t lit
– Combination of qualities forming a more complex query; e.g.,
discharge = area × velocity
Specify the participant based on their ‘role’ with respect to a
perdurant
– amount of water & a particular ground surface amount of soil as
amount of water & a particular ground surface, amount of soil as
participants in the infiltration process.
Describe social hydro‐concepts, e.g. catchment
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13. What’s Next….
Can we formalize this?
Can we formalize this?
A flash flood is a rapid flooding of geomorphic low‐lying areas ‐ washes,
rivers, dry lakes and basins. It may be caused by heavy rain or meltwater
from ice or snow flowing over icesheets or snowfields. Flash floods can also
from ice or snow flowing over icesheets or snowfields Flash floods can also
occur after the collapse of an ice dam, debris dam or a human structure,
such as a dam. Flash floods are distinguished from a regular flood by a
timescale less than six hours.1
* http://en.wikipedia.org/wiki/Flash_flood 13
14. Conclusions
Property
Object (Spatial,
Matter Temporal)
Process
Event
Semantic Integration of Geo‐Sensor Data
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