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The integration of semantic technologies and TV services is a substantial innovation to improve the services to users in an environment that is extended beyond the fixed home environment. But currently, this integration is mainly limited to provide personalized recommendation services and systems by matching user static preferences. Designing and development of interactive TV (iTV) applications using semantic technologies are not realized yet. In this work, we explore the potential of introduction semantic technologies and smart spaces in design and development of iTV applications. We use an example scenario to show how future iTV applications include the mesh-up of information from different sources. We proposed a methodology and show how ontology-driven approach can help to design and develop these iTV applications. We demonstrate the suitability of our ontology-driven application development tools and rule-based approach for the development of highly dynamic context-aware iTV applications. [ABSTRACT FROM AUTHOR]

Highlights: [•] Human activity modelling ontologies for imprecise knowledge reasoning are inexistent. [•] We develop a fuzzy ontology to deal with uncertain or vague knowledge representation. [•] We show examples of use of the ontology in different scenarios within office domain. [•] We create a dataset with rules and queries and compare fuzzy and crisp reasoning. [•] We demonstrate that fuzzy ontological reasoning improves accuracy and it is scalable. [Copyright &y& Elsevier]

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. [ABSTRACT FROM AUTHOR]