Type-2 fuzzy ontology-based semantic knowledge for indoor air quality assessment

Semantic web technology plays an increasing role in performing smart home applied programs and it has led to improve semantic interoperability among different systems. However, classical ontologies fail to illustrate ambiguous, incomplete, and uncertain knowledge often available in the real world. On the other hand, the air quality assessment carried out to determine “the degree of pollution” lacks accurately specified boundaries; therefore, the conventional approach based on classic ontology cannot extract real-valued memberships and consequently fails to support ambiguous, incomplete, and uncertain knowledge. Integrating semantic web of things technology (SWOT) and type-2 fuzzy logic improves the capability of semantic reasoning to retrieve query information. Annotation of sensor-generated data and the ability to infer and represent knowledge based on type-2 fuzzy logic are extremely essential when the available data are ambiguous and uncertain. Hence, in this paper, we have provided a framework to build an IoT-based home air quality assessment system by using type-2 fuzzy ontology so that smart home systems can make a decision and control appropriately based on predefined rules by employing the provided semantic reasoning. •Classical ontologies in itself are not able to share ambiguous and indefinite data.•Semantic interoperability enables systems to process in a meaningful way the information.•Type-2 fuzzy ontology significantly increases the indoor air quality assessment accuracy.•Type-2 fuzzy sets are useful in the environments which contain ambiguity and uncertainty.•Using semantic technology and type-2 fuzzy logic improves the semantic reasonability of query information retrieval.