A Reasoning Approach of Human Activity Based on Ontology Model in Home Intelligent Space To solve the problem of lack of representation and reasoning of knowledge in human activity understanding in home environment, the activity reasoning method based on ontology model in intelligent space is presented. Firstly, a knowledge model of human activity domain based on the ontology technology is established. Then, the dataconcept conversion mechanism centered on the humanobject interaction relationship is applied to realize the conceptual representation of activity knowledge in intelligent space. On this basis, in order to make the best of activity domain knowledge, we use the semantic network rule language to build a knowledge rule base of human activity domain. The complex human activities in the current state are inferred by matching the realtime ontology in home intelligent space and the knowledge in rule base. Finally, the experimental results show that the method can effectively realize the reasoning of human activity.

Modeling and reasoning of IoT architecture in semantic ontology dimension Abstract   The architecture for IoT is the primary foundation for designing and implementing the System of Internet of things. This paper discusses the theory, method, tools and practice of modeling and reasoning the architecture of the Internet of Things system from the dimension of semantic ontology. This paper breaks the way of static ontology modeling, and proposes an implementation framework for realtime and dynamic ontology modeling of the IoT system from the running system. According to the actual needs of the health cabin IoT system and the combination of theory and practice, the system architecture model of the semantic ontology dimension of IoT is built. Then, based on the reasoning rules of the ontology model, the model is reasoned by Pellet reasoning engine which injects the atom of the custom reasoning builtins into the source code. In this way we have realized the automatic classification and attribute improvement of resources and behaviors of the IoT system, the realtime working state detection and fault diagnosis of the IoT system, and the automatic control of the IoT system and resources.