Modeling Events for Integrating Event-Based Information

A Model of Events for Integrating
Event-Based Information in Complex
Socio-technical Information Systems
Ansgar Scherp, Thomas Franz, Carsten Saathoff, Steffen Staab
Institute WeST
University of Koblenz
Germany

http://west.uni-koblenz.de/

Emergency Response Scenario
Calls to report about
a power outage
Fire Department

Coordinate and Emergency Citizen
keep up to Documentary
Report Hotline support
date
and update
• Several emergency response entities are involved
about the incident
Creates incident
with audio recording
• Using different event-based systems
Reports Emergency Report and update
• Common understanding of exchanged multimediaincident
by taking
photos
Control Center about the

information is needed to efficiently communicate
etc. Coordinate
and keep up
between ER entities
Request to
to date
Police Department
Forward
report about a
Liaison flooded cellar Emergency Response
Officer Coordination

 EU Integrated Project WeKnowIt
http://www.weknowit.eu/ Snapped pole image from:
http://www.dailymail.co.uk/

Web Science and Ansgar Scherp Event-Model-F
Technologies scherp@uni-koblenz.de Slide 2

Outlook

 Emergency Response Scenario
 Motivation
 Formal Model of Events
 Existing Event Models
 Future Work


Motivation

 Events need to be modeled and are useful
in a variety of application domains
 Lifelogs, multimedia experience sharing
 Emergency response
 Cultural heritage
 News
 Sports
 Surveillance
 …
 However
 Event detection and annotation from different sources
 Using different data models and proprietary solutions
 Event descriptions need to be shared between systems

Event-Model-F

• Humans like to think in terms of events & entities
• Human-centered approach to capture
experience and knowledge

• Events
• Occurrences in which humans participate
• Subject to interpretation and discussion

• Development of core ontology Event-Model-F
• Sophisticated modeling support for occurrences in which
humans participate
• Homage to event model E by Westermann & Jain

Requirements to a Common Event Model

• Participative aspect
• Temporal aspect
• Spatial aspect
• Structural aspect
• Mereology (composition)
• Causality
• Correlation
• Interpretation
• Experiential aspect (documentation)


Comparison to Existing Event Models

SsVM = Semantic-syntactic video model
VERL = Video event representation language
CIDOC CRM = Conceptual reference model for cultural heritage
Web Science and Ansgar Scherp Event-Model-F 7

Ontology Patterns of Event-Model-F
• Event-Model-F defines six core ontology patterns based
on Description and Situation pattern
(1) Participation pattern
(2) Mereology pattern (composition)
(3) Causality pattern
(4) Correlation pattern
(5) Documentation pattern
(6) Interpretation pattern

• Specified in Web Ontology Language (OWL)
• Formalized in Description Logics
• Graphical representation in UML-like notation


Modeling Basis of Event-Model-F

• Ontology pattern Descriptions and Situations (DnS) as
fundamental design principle for Event-Model-F
• Formal representation of context through use of roles
• Decoupling concrete events and objects from their roles in a
specific contextual situation

• Description
• Specification of roles required in a specific situation
• Can be understood as template
• Situation
• Observable real-world situation, i.e., a concrete
combination of events and objects
• Satisfies a description, if it fits into the template


Example: Descriptions and Situations Pattern

• DomesticPowerOutage- • DomesticPowerOutage-
Description defines roles Situation defines objects
• AffectedBuildingRole House-1 : Object
• AffectedPersonRole Paul-1 : Object,
• … Classify Sandy-1 : Object,…
…

• Important: Different people may claim
different causes for the outage
• Different interpretations of the same
DomesticPowerOutageSituation
a) Snapped power pole
b) Problem with the power plant
Image source: Wikipedia

(3) Causality Pattern

• Event (cause) implies other event (effect)
• Causal relationship holds under some justification
• Causes and effects are events, and only events

EventCausalityDescription Concept
defines
defines exactly 1 Cause
EventCausalityDescription
Role
defines exactly 1 Effect EventRole

defines exactly 1 Justification
Description
defines only (Cause or Effect Effect
Cause
or Justification)
Justification
satisfies
isSatisfiedBy exactly 1 EventCausalitySituation
classifies
isRoleOf
Situation
Event Description
Example: The event of a snapped power pole causes a
isEventIncludedIn
isObjectIncludedIn
power outage.
EventCausalitySituation


(1) Participation Pattern
• Participation of living and non-living objects in events
• Reuse of domain knowledge
EventParticipationDescription
defines exactly 1 DescribedEvent
defines min 1 Participant
defines some LocationParameter
defines some TimeParameter
Roles the
defines only (DescribedEvent or Participant or
entities play
LocationParameter or TimeParameter)
isSatisfiedBy exactly 1 EventParticipationSituation
Real world
entities
Example: Firemen and home owner are involved in an
incident of a house fire.


(2) Mereology Pattern
• Composite event consists of multiple component events
• Composition along time, space, and space-time
defines
EventCompositionDescription
EventCompositionDescription Concept Parameter
defines exactly 1 Composite
Description EventRole
defines min 1 Component isParameterFor
EventCompositionConstraint

defines only (Composite or Component or
EventCompositionConstraint)
Composite Component TemporalConstraint

classifies classifies parametrizes
isSatisfiedBy exactly 1 EventCompositionSituation SpatioTemporalConstraint
satisfies Time-Interval parametrizes
Event
hasParticipant Spatio-Temporal-Region
isEvent
Example: Events of a snapped power pole, power
IncludedInObject
SpatialConstraint

outage, and bursting ofhasQuality are components ofparametrizes
Situation
hasQuality
a dam a
larger flooding event.Quality hasRegion
isTime
Space-Region
isSpaceTime isSpace
EventCompositionSituation IncludedIn IncludedIn IncludedIn


(4) Correlation Pattern
• Correlate events have a common cause
• Happen at the same time or share some overlap
• Useful, as often only correlation is observable and the
common cause remains unknown
defines
EventCorrelationDescription
EventCorrelationDescription

defines min 2 Correlate Concept

defines exactly 1 Justification
Description EventRole Role
defines only (Correlate or Justification)
satisfies
isSatisfiedBy exactly 1 EventCorrelationSituation
Correlate Justification
classifies classifies
Situation
Event Description
Example: Several correlating power outage events
isEventIncludedIn
happen in the city.
EventCorrelationSituation isObjectIncludedIn


(5) Documentation Pattern

• Provide documentary evidence for an event
• Annotation of events with photos, video, audio, etc.

EventDocumentationDescription
defines exactly 1 DocumentedEvent
defines some Documenter
defines only (DocumentedEvent or Documenter)
isSatisfiedBy exactly 1 EventDocumentation-
Situation

Example:
• Documenter classifies ImageData defined in COMM
(Core Ontology on Multimedia)
• Formal model of MPEG-7 low-level descriptors


(6) Interpretation Pattern
• Explicit modeling of contextual views on events
• Combines the instantiations of patterns (1) to (5)
EventInterpretationDescription defines
EventInterpretationDescription
defines exactly 1 Interpretant
Role
defines min 1 RelevantSituation RelevantSituation
Description
defines only (Interpretant or RelevantSituation)
Domain Ontology
isSatisfiedBy exactly 1 EventInterpretationSituation
EventRole RelevantComposition

RelevantCausality
satisfies Interpretant
RelevantCorrelation
For example: Interpretation of a power outage
classifies RelevantParticipation
• Citizen: power outage on our street is caused by snapped
classifies
power pole
Situation

• Officer: power outage of the city is caused by a problem
Event Situation

isEventIncludedIn
in the power plant
EventInterpretationSituation
isObjectIncludedIn


Design Approach

1. Chose of foundational ontology DOLCE+DnS Ultralight as
modeling basis
• Aims at capturing the most essential aspects in the world
• Defines disjunctive upper classes
Event, Object, Quality and Abstract
• Follows a pattern-oriented approach for ontology design
2. Use of ontology design patterns
• Generic solution to recurring modeling problem
• Reduces complexity of the designed model
3. Defining Event-Model-F as core ontology
• Provides structural knowledge that spans across multiple
domains, e.g., lifelogs, emergency response, etc.
• Build on top and align it with DOLCE+DnS Ultralight


Comparison to Existing Event Models

• Event Model E, EventML, Event Calculus, CIDOC CRM,
VERL, SsVM, Event Ontology, Eventory

• Do not follow such a systematic development approach
• Semantically ambiguous
• Conceptually narrow
• Hinders interoperability of different event-based systems


SemaPlorer

Place Object
Type Event


Future work on Event-Model-F

• Extraction of events and objects from Web content
• Reasoning on Event-Model-F with Linked Geo Data

• Event-Model-F Website
• Provides the ontology and examples in OWL
• Implementation of Java API
• http://west.uni-koblenz.de/eventmodel/


Thank you for your attention!
Questions?

Ansgar Scherp
scherp@uni-koblenz.de
http://west.uni-koblenz.de/


---


What is an event?

• Events
• Perduring entities that unfold over time
• Occurrences in which humans participate
• Subject to discussions and interpretations by humans

• Objects
• Enduring entities that unfold over space

• Events and objects require each other


Ontology Stack
• Domain Ontologies
• Cover a specific domain
• Example: fishery, human body, emergency response, etc.
• Core Ontologies
• Coverage: span across multiple domains
• Examples: annotation, communication, events, ...
• Foundational Ontologies
• Span across multiple core ontologies
Domain
Ontologies
Core
Ontologies

Foundational Ontologies


Non-functional Requirements
• Extensibility
• Include future aspects for describing events
• Axiomatization & formal precision
• Required for a common understanding of events
• Interoperability between systems
• Modularity
• Reduce complexity by selecting only what is required
• Reusability
• Share common events/objects for different interpretations
• Reuse of domain knowledge
• Separation of concerns
• Core model needs to be applicable in many different domains
• Separate structural knowledge from domain-specific knowledge


Non-functional Requirement

• Extensibility
• Pattern-oriented approach of DOLCE+DnS Ultralight
• Specializing/extending existing patterns, adding new patterns, …
• Axiomatization & formal precision
• Foundational ontology DOLCE+DnS Ultralight as basis
• Semantically precise through Description Logics
• Modularity
• Pattern-oriented design
• Reusability
• Integrating existing domain ontologies
• Separation of concerns
• Structural knowledge is defined in the ontology design patterns
• Domain-specific knowledge is linked through classifying roles

Event-Model-F API
• Programming interface to Event-Model-F
• Enable direct use of the Event-Model-F without requiring
to know the internal details of the ontology
• Layered architecture of the API

Your Application
Event-Model-F Extended API
Event-Model-F Core API
RDF Storage (Sesame)

• Release under open source license
https://launchpad.net/eventmodelf


Short Example: Serious Weather Conditions
• During serious weather conditions a flood happens
• Causality: power pole snappes and causes a power outage
• Participation: citizen observes this event from his home


Modeling Events for Integrating Event-Based Information

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