Anusuriya Devarajuanusuriya.devaraju@uni-muenster.de

1 • Background and Motivation

2 • Research Goals and Questions

3 • Methods

4 • Theory (SEGO)

5 • Application

6 • Evaluation

7 • Conclusions and Future Work

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1 • Background and Motivation

2 • Research Goals and Questions

3 • Methods

4 • Theory (SEGO)

5 • Application

6 • Evaluation

7 • Conclusions and Future Work

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The Sensor Revolution

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In 2010, US Government spent around US$500 million

on the maintenance and operation of environmental

monitoring technology networking….Crowd sourcing for Pakistan Flood Relief

The Sensor Web

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Lots of Data, and No Information?

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Sensors Tell More than They Sense!

1National Science Foundation Report, 2004.

“Sensors enables an understanding of environmental variability and change”1

Their observations reflect the influence of geographic occurrences operating in the environment.

Basic Assumption :

An Example: Flood Stage and Inundation

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The Challenge

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How can we infer information about geographic occurrences from sensor observations?

Formal specifications in the Semantic Sensor Web represent information about sensors and observations, but they lack details about geographic occurrences.

Sensors and Observations Modeling

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(Existing work on modelling sensors and observations)

A Functional

Ontology of O&M

An Ontological

Analysis of O&M

The Challenge

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Terminological Disagreements

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“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, andvice versa” (Worboys:2005)

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)

Event Specifications in the Semantic Web

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a. Address a specific type of occurrenceb. The occurrence-of-interest is not associated with

sensing concepts.

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

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Research Goals

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Develop an ontology to capture their relations.

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2Exploit the ontological vocabularies with reasoning mechanisms to make inferences about geographic events.

Ontology in a Nutshell

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InformalImplicit

Formal ExplicitShared

represents

Research Questions

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What are the basic representational requirements of geographic occurrences in the context of the sensing domain?

How can geographic occurrences be formally modelled with respect to properties observed by sensors?

How can ontologies support the reasoning about geographic occurrences from sensor observations?

Requirements gathering

Formal specification : Sensing Geographic Occurrences (SEGO)

Proof-of-concept implementation

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

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The Domain of the Ontology

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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.

Research Methods

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a. Review existing theories on occurrence and observation to identify the key aspects of geographic occurrences.

b. Develop an ontology to represent the relations between geographic occurrences and observations.

c. Design and implement a use case; verify the use case results.

d. Evaluate the ontology by comparing it with an alternative approach in the Sensor Web.

e. Evaluate the research as a whole from a System Development perspective.

(Repeat steps 1-3 if necessary)

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An Overview of SEGO

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a. Middle-out ontology development approach (Uschold:1996).

b. Competency questions (Gruninger:1994).

The Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) Ontology

SWRL Temporal Ontology

Blizzard application ontology

Sensing Geographic Occurrences Ontology (SEGO)

Top Level

Domain Level

Application Level

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

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Key Concepts of Geographic Occurrences

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An analogy betweenevents and objects,

and between processes and matter.

EXP/HIST perspectives (Galton:2006), Stimulus-centric approach (Kuhn:2009)

geo-process geo-event

geo-stimulus

temporally-made-of

temporal-sub-event-of

participant-in

physical-object

From Observations to Occurrences

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actuatesactuates

An ongoing air flow process acts

as a stimulus

A demarcated, inferredhigh wind event

(windspeed ≥ 40mph)

windspeed

An anemometer as a sensor

SEGO

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Theoretical Insights

a. Events and processes are distinguished by means of their temporal shapes and their relations to a sensor.

b. Their relations are modeled after the analogy between events and objects, and between processes and matter.

c. Functional participatory relations - relevant for querying information in the Sensor Web.

d. The location of an occurrence is determined by the location of its participants – this does not apply to all cases!

e. A feature-of-interest is regarded as an “identifiable” real-world object regarding which an observation is performed.

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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

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Blizzard – Why It’s a Big Deal..

Figure Source : http://monroetalks.com/forum/index.php?topic=12465.0 28

Definition

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Identifying Blizzards

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A method for identifying blizzards (Lawson:2003).

Weather observations supplied by the Climate Data Online1.

1http://www.climate.weatheroffice.gc.ca/climateData/canada_e.html

Blizzard Application Ontology

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Combining Rules and Ontologies

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1. Domain-Specific Rules

Relating an observation event to its feature-of-interest

2. Application-Specific RulesIdentifying 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)

observation-event(?e) ⋀ observed-property(?p) ⋀feature-of-interest(?f) ⋀ has-obs-property(?e,?p) ⋀has-bearer(?p,?f) has-foi(?e,?f)

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System Implementation

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System architecture.

A SPARQL query example.

A time-line view.

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

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Use Case Results Verification

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A blizzard event report published by the Environment Canada.

A tabular view of the inferred events.

Station name : Brandon, Manitoba

Test Data : Hourly observations (Nov-Mac, 1958-1965); 14 blizzard events

An Evaluation Against SemSOS

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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?

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?

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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

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Contributions

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Applications of rules-based reasoning and event-based

querying.

Building blocks for developing application ontologies

A formal specification that captures the relations between geographic occurrences

and observations to support inferences of the former from the latter.

What’s Next?

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Develop test cases.

Represent different interpretations of the same occurrence.

Reasoning about events across different sensors.

Model causality.

Event-oriented querying in the Sensor Web.

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Thank You

41SEGO Website : http://observedchange.com/ontologies/sego/

For more information, please visit:

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