Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making. In order to infer meaningful information about occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. This thesis develops an ontology to formally capture the relationships between geographic occurrences and the properties observed by in situ sensors. Building upon the existing positions on experiential and historical perspectives, stimulus-centric sensing, event-process algebra and thematic roles, the ontology elucidates the key concepts associated with geographic occurrences that are particularly significant from a sensing point of view. A use case for reasoning about blizzards and their temporal parts from real time series supplied by the Environment Canada illustrates the ontological approach. This thesis evaluates its findings on the basis of a comparison with an alternative approach in the Sensor Web, a verification of the use case results using an official event report published by the weather agency and an analytical assessment approached from the system development perspective. The theoretical contribution of the thesis lies in the development of a formal model, which constitutes common building blocks for constructing application ontologies that account for inferences of geographic events from observations. With regards to its practical contribution, the thesis has demonstrated how ontological vocabularies are exploited with reasoning mechanisms to infer information about events, and to formulate symbolic spatio-temporal queries.

1. Anusuriya Devaraju
anusuriya.devaraju@uni-muenster.de

2. 1 • Background and Motivation
2 • Research Goals and Questions
3 • Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
2

3. 1 • Background and Motivation
2 • Research Goals and Questions
3 • Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
3

4. The Sensor Revolution
In 2010, US Government spent around US$500 million
on the maintenance and operation of environmental
Crowd sourcing for Pakistan Flood Relief monitoring technology networking….
4

5. The Sensor Web
5

6. Lots of Data, and No Information?
6

7. Sensors Tell More than They Sense!
“Sensors enables an understanding of environmental
variability and change”1
Basic Assumption :
Their observations reflect the influence of
geographic occurrences operating in the environment.
1National Science Foundation Report, 2004. 7

8. An Example: Flood Stage and Inundation
8

9. The Challenge
How can we infer information about geographic
occurrences from sensor observations?
9

10. Sensors and Observations Modeling
Formal specifications in the Semantic Sensor Web
represent information about sensors and observations,
but they lack details about geographic occurrences.
A Functional An Ontological
Ontology of O&M Analysis of O&M
(Existing work on modelling sensors and observations)
10

11. The Challenge
11

12. Terminological Disagreements
In GI Science, while the necessity for handling
temporal phenomena has been acknowledged for
some time now, progress has been hampered by the
lack of principled ways of describing these events
and processes...(Galton:2008)
“An event is comprised of processes” (Yuan:2001)
“A process is composed of events” (Lemosdias et al.:2004)
“One person’s process is another’s event, and
vice versa” (Worboys:2005)
12

13. Event Specifications in the Semantic Web
a. Address a specific type of occurrence
b. The occurrence-of-interest is not associated with
sensing concepts.
13

14. 1 • Background and Motivation
2 • Research Goals and Questions
3 • Scope and Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
14

15. Research Goals
Develop an ontology to capture
1
their relations.
Exploit the ontological vocabularies
2
with reasoning mechanisms to make
inferences about geographic events.
15

16. Ontology in a Nutshell
Formal
represents
Explicit
Informal Shared
Implicit
16

17. Research Questions
Requirements gathering
What are the basic representational requirements of
geographic occurrences in the context of the sensing domain?
Formal specification : Sensing Geographic Occurrences (SEGO)
How can geographic occurrences be formally modelled with
respect to properties observed by sensors?
Proof-of-concept implementation
How can ontologies support the reasoning about geographic
occurrences from sensor observations?
17

18. 1 • Background and Motivation
2 • Research Goals and Questions
3 • Scope and Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
18

19. The Domain of the Ontology
a. Represent information about geographic occurrences from a
sensing point of view.
b. Institutionalized events are considered as the primary mode
of occurrence identification.
c. Observations are produced by an in-situ sensor.
19

20. Research Methods
a. Review existing theories on occurrence and observation to
1
identify the key aspects of geographic occurrences.
b. Develop an ontology to represent the relations between
2
geographic occurrences and observations.
c. Design and implement a use case; verify the use case
3
results.
d. Evaluate the ontology by comparing it with an alternative
4
approach in the Sensor Web.
e. Evaluate the research as a whole from a System
5
Development perspective.
(Repeat steps 1-3 if necessary)
20

21. An Overview of SEGO
a. Middle-out ontology development approach (Uschold:1996).
b. Competency questions (Gruninger:1994).
Top Level
The Descriptive Ontology for Linguistic and
Cognitive Engineering (DOLCE) Ontology
Domain Level
SWRL
Sensing Geographic
Temporal
Occurrences Ontology (SEGO)
Ontology
Application Level
Blizzard application ontology
21

22. 1 • Background and Motivation
2 • Research Goals and Questions
3 • Scope and Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
22

23. Key Concepts of Geographic Occurrences
EXP/HIST perspectives (Galton:2006), Stimulus-centric approach (Kuhn:2009)
temporal-sub-event-of
physical-object
geo-process geo-event
temporally-
made-of
participant-in
geo-stimulus
An analogy between
events and objects,
and between
processes and matter.
23

24. From Observations to Occurrences
An ongoing air A demarcated, inferred
flow process acts high wind event
as a stimulus (windspeed ≥ 40mph)
actuates
actuates
An anemometer
as a sensor
windspeed
24

25. SEGO
25

26. Theoretical Insights
1 Events and processes are distinguished by means of their
a.
temporal shapes and their relations to a sensor.
2 Their relations are modeled after the analogy between events
b.
and objects, and between processes and matter.
3 Functional participatory relations - relevant for querying
c.
information in the Sensor Web.
4 The location of an occurrence is determined by the location of
d.
its participants – this does not apply to all cases!
5 A feature-of-interest is regarded as an “identifiable” real-world
e.
object regarding which an observation is performed.

27. 1 • Background and Motivation
2 • Research Goals and Questions
3 • Scope and Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
27

28. Blizzard – Why It’s a Big Deal..
Figure Source : http://monroetalks.com/forum/index.php?topic=12465.0 28

29. Definition
29

30. Identifying Blizzards
Weather observations supplied A method for identifying blizzards
by the Climate Data Online1. (Lawson:2003).
1http://www.climate.weatheroffice.gc.ca/climateData/canada_e.html
30

31. Blizzard Application Ontology
31

32. Combining Rules and Ontologies
1. Domain-Specific Rules
1
Relating an observation event to its feature-of-interest
observation-event(?e) ⋀ observed-property(?p) ⋀
feature-of-interest(?f) ⋀ has-obs-property(?e,?p) ⋀
has-bearer(?p,?f) has-foi(?e,?f)
2. Application-Specific Rules
2
Identifying different types of blizzard
blizzard(?b) ⋀ extreme-blowing-snow(?bs) ⋀
snow-event(?s) ⋀ temporal-sub-event-of(?bs,?b) ⋀
temporal-sub-event-of(?s,?b) traditional-blizzard(?b)
32

33. System Implementation
A SPARQL query example.
System architecture.
A time-line view.
33

34. 1 • Background and Motivation
2 • Goals and Research Questions
3 • Scope and Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
34

35. Use Case Results Verification
Station name : Brandon, Manitoba
Test Data : Hourly observations (Nov-Mac, 1958-1965); 14 blizzard events
A blizzard event report published
A tabular view of the inferred events.
by the Environment Canada.
35

36. An Evaluation Against SemSOS
Competency Questions SemSOS SEGO
Sensor and observations
What are the wind-speed values and their observed time produced by station A on YYYY-
MM-DD? Identify the maximum and minimum values.
Events, sensing and temporal information
What are the observed values associated with the blizzard detected by [station id/name]
on YYYY-MM-DD?
Are there any ground blizzards detected by station A between YYYY-MM-DD and YYYY-
MM-DD?
Interrelation between events
How long does the blowing snow event last during the blizzard detected at station A on
YYYY-MMDD?
Participating entities
What are the atmospheric features involved in the snow event X?
36

37. Analytical Research Evaluation
An evaluation approached from the System Development
perspective (Burstein and Gregor: 1999).
a. Significance
b. Internal and external validity
c. Objectivity
d. Reliability
Is there theoretical and practical significance?
Have rival methods been considered?
Are the findings congruent with or connected to prior theory?
Are the study’s methods described in detail?
Are the research questions clear?
37

38. 1 • Background and Motivation
2 • Research Goal and Questions
3 • Scope and Methods
4 • Theory (SEGO)
5 • Application
6 • Evaluation
7 • Conclusions and Future Work
38

39. Contributions
Building blocks for developing Applications of rules-based
application ontologies reasoning and event-based
querying.
A formal specification that captures the
relations between geographic occurrences
and observations to support inferences
of the former from the latter.
39

40. What’s Next?
1 2 Represent different interpretations of the
Develop same occurrence.
test cases. 3 Model causality.
4 Reasoning about events
across different sensors.
5 Event-oriented
querying in the
Sensor Web.
40

41. Thank You
For more information, please visit:
SEGO Website : http://observedchange.com/ontologies/sego/ 41

42. 42