Ontology engineering and reasoning to support real world human behavior recognition

Abstract

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.