Abstract
Events are central aspect of many semantic ambient media applications such as surveillance, smart homes, automobiles, and others. Existing models for events typically do not follow a systematic development approach, are conceptually narrow with respect to event features, and their semantics is often ambiguous. This makes the communication between and integration of different event-based components and event-based semantic ambient media applications a challenging task. In this paper, we present the Event-Model-F, a formal model of events based on the foundational ontology DOLCE+DnS Ultralite (DUL). The Event-Model-F provides comprehensive support to represent time and space, objects and persons, mereological, causal, and correlative relationships between events, and different interpretations of the same event. It is developed following a pattern-oriented ontology design approach and can be easily extended by domain specific ontologies. We introduce the design and implementation of an application programming interface that allows for easy integration of the Event-Model-F in arbitrary applications. The use of the Event-Model-F is demonstrated at the example of a socio-technical system of emergency response and implemented in the SemaPlorer+ + application for creating and sharing event descriptions.
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http://ontologydesignpatterns.org/wiki/Ontology:DOLCE+DnS_Ultralite, last retrieved: 4 August 2010.
http://www.weknowit.eu/, last retrieved: 4 August 2010.
http://www.flickr.com/, last retrieved: 4 August 2010.
http://www.geonames.org/, last retrieved: 4 August 2010.
http://wordnet.princeton.edu/, last retrieved: 4 August 2010.
http://www.foaf-project.org/, last retrieved: 4 August 2010.
DOLCE provides a similar property between endurants and perdurants (objects and events) called participant-in.
http://wiki.loa-cnr.it/index.php/LoaWiki:Ontologies, last retrieved: 4 August 2010.
In the case of sociology, causes and effects are states [23].
A good overview provides the following website: http://semanticweb.org/wiki/Tripresso, last retrieved: 4 August 2010.
http://www.openrdf.org/doc/elmo/1.3/, last retrieved: 4 August 2010.
http://www.incunabulum.de/projects/it/owl2java, last retrieved: 4 August 2010.
http://www.openrdf.org/, last retrieved: 4 August 2010.
http://www.w3.org/2003/01/geo/, last retrieved: 4 August 2010.
Organization for the Advancement of Structured Information Standards, http://www.oasis-open.org/, last retrieved: 6 August 2010.
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Acknowledgements
This research has been co-funded by the EU in FP6 in the X-Media project (026978) and FP7 in the WeKnowIt project (215453). We kindly thank Peter Whitwam and Keith Bradley from the Emergency Planning Team of the City Council of Sheffield, UK for the discussions on emergency planning and emergency response and the requirements and feedback on the SemaPlorer+ + application. We thank our student Daniel Schmeiß for his support in implementing the Event-Model-F API and SemaPlorer+ + application.
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Appendix A: Axiomatization of the Event-Model-F in description logic
Appendix A: Axiomatization of the Event-Model-F in description logic
In the following, we describe the axiomatization of our Event-Model-F in Description Logics [4] along the different patterns for participation, composition, causality, correlation, and interpretation. A discussion on the axiomatization of ontology design patterns in general is conducted at the example of the causality pattern in Section 6.
1.1 A.1 Participation pattern
The participation pattern is discussed in Section 5.1. It describes the set of objects that participate in an event and are relevant in a given context. The participation pattern defines that for one event there has to be at least one participant. This means that there are no events without a participating object. We formalize this with the following set of axioms:
1.2 A.2 Mereology pattern
The composition pattern defines how events are composed, i.e., it basically describes a part-whole relationship between events that is valid in a certain context and is possibly subject to a set of constraints (cf. Section 5.2). We require exactly one composite event, i.e., the whole, and at least one component, i.e., the part. The specification of constraints is optional.
1.3 A.3 Causality pattern
The causality pattern defines a causal relationship by exactly one cause, exactly one effect, and exactly one justification. The pattern is described in Section 5.3. A formal axiomatization is provided below.
1.4 A.4 Correlation pattern
The correlation pattern describes the correlation of a set of events, as discussed in Section 5.4. It only makes sense to specify a correlation between two or more events. Further, the correlation description also refers to the justification defined for the causality pattern.
1.5 A.5 Documentation pattern
The documentation pattern provides the documentation of an event by arbitrary sensory data such as images, video, and audio as well as other events. Thus, it allows to specify for an event by which objects and events it is documented. The pattern has been discussed in Section 5.5. A formal axiomatization is provided below.
1.6 A.6 Interpretation pattern
The interpretation pattern defines an interpretation of exactly one event. Therefore, it provides the means to specify all those patterns for an event that are relevant for the interpretation. We have discussed the pattern in Section 5.6 and provide the formal axiomatization below.
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Scherp, A., Franz, T., Saathoff, C. et al. A core ontology on events for representing occurrences in the real world. Multimed Tools Appl 58, 293–331 (2012). https://doi.org/10.1007/s11042-010-0667-z
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DOI: https://doi.org/10.1007/s11042-010-0667-z