Bayesian network and ontology combined reasoning method capable of self-perfecting network structure

Abstract

The invention discloses a Bayesian network and ontology combined reasoning method capable of self-perfecting network structure. The Bayesian network and ontology combined reasoning method comprises the following steps of: firstly, forming a certain logical relation for a low-level context based on the ontology; secondly, inputting a result which serves as a father node of the Bayesian network; and finally, inferring future behavior habits of a user through the Bayesian network. In the method, the Bayesian network structure and an ontology model are dynamically adjusted with the feedback of the user; personalized service is provided for the user, the personal ontology model is established, and a totally-personal context inference system model can be formed after learning for a period of time; and by directly inputting the father node of the Bayesian network, the inference accuracy of the Bayesian network is improved, so that the speed and accuracy rate of the whole inference process are greatly improved.

Description

technical field [0001] The invention relates to a Bayesian network and ontology joint reasoning method used for context reasoning and capable of autonomously improving the network structure, belonging to the technical field of context-aware computing. Background technique [0002] In context-aware technology, context reasoning is the core process. On the one hand, in a smart space, the data perceived by sensing devices such as sensors are all low-level contextual information. For such a large amount of data without any modification, especially in the case of multi-sensor cooperative sensing, data inconsistency and inconsistency are likely to occur. In the case of accuracy, instability, and ambiguity, the data uploaded by different sensors may even contain contradictory and wrong information, which cannot directly affect top-level decision-making. It must go through the process of filtering and inferring the data The process of contextual reasoning such as integration and fu...

AI Technical Summary

Problems solved by technology

[0012] In the process of joint reasoning based on ontology and Bayesian network, some current research methods directly scale and complete the ontology model to form a fixed ontology model, and then use the ontology model to directly generate the structure of the Bayesian network. That is, the structure of the Bayesian network is learned from the ontology model, and the network structure is fixed, resulting in the Bayesian network only performing probability calculations during the inference process. This method fails to take advantage of the Bayesian network ; Some studies make the Bayesian network learn independently, form the Bayesian network structure through expert systems, prior knowledge, etc., and combine other methods (such as predicate logic, rule reasoning) for joint reasoning. The complexity is large; most of the Bayesian network research focuses on the learning of the Bayesian network structure, and the calculation of the node probability mostly uses the training data method, ignoring the application of the ontology model reasoning results, thus reducing the Bayesian network structure. Inference Accuracy

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