Ontology engineering and reasoning to support real world human behavior recognition
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Villalonga Palliser, ClaudiaEditorial
Universidad de Granada
Departamento
Universidad de Granada. Programa Oficial de Doctorado en: Tecnologías de la Información y la ComunicaciónMateria
Ontología Conducta Reconocimiento automático Genética de la conducta Biosensores Computación sensible al contexto
Materia UDC
159.9 16 6106
Date
2016Fecha lectura
2016-12-16Referencia bibliográfica
Villalonga Palliser, C. Ontology engineering and reasoning to support real world human behavior recognition. Granada: Universidad de Granada, 2016. [http://hdl.handle.net/10481/44536]
Sponsorship
Tesis Univ. Granada. Programa Oficial de Doctorado en: Tecnologías de la Información y la ComunicaciónAbstract
This thesis further proposes the Mining Minds Context Ontology, an
OWL ontology for exhaustively modeling rich and meaningful expressions
of context. This ontology enables any combination of cross-domain
behavior primitives, also referred to as low-level contexts, in order to
infer more abstract human context representations, also called highlevel
contexts. The context ontology extends beyond the state-of-theart
while uniting emotion information as a novel behavioral component
together with activity and location data to model new contextual information.
An ontological method based on descriptive logic is developed
for deriving high-level context information out of the combination of
cross-domain low-level context primitives, namely activities, locations
and emotions. The proposed method not only proves e cient while deriving
new contextual information but also robust to potential errors
introduced by low-level contexts misrecognitions. This method can be
used for determining any type of high-level context information from
diverse sources of low-level context data. Thus, it can be easily applied
to any new domain, only requiring the extension of the ontology itself.
The proposed models and methods enable comprehensive descriptions
and dynamic selection mechanisms for heterogeneous sensing resources
to support the continuous operation of behavior recognition
systems; likewise, exhaustively descriptions and automatic inference of
abstract human context information is supported to enhance the operation
of behavior-aware systems. Hence, these ontologies and ontology
reasoning-based methods pave the path to a new generation of behavior
recognition systems readily available for their use in the real-world.