Dynamic ontologies evaluation framework using quantum perceptron neural network

Ontologies can be built on systems that can evolve continuously. Ontologies analysis and appraisal are performed for measuring the eminence of an ontology. Ontologies evaluation is used to automatically reveal conceivability problems. For evaluating dynamic ontologies in semantic web and to set the weights to the neurons, Classical Neural Network (CNN) has to face a number of challenges such as the absence of discrete algorithm, limited memory capacity, time-consuming training due to the declarative nature of ontologies. CNN cannot provide low-cost learning. In CNN, data is non-linear and hard to analyze. Recently, with the rapid development of technology, there are a lot of applications such as dynamic ontologies creation and evaluation require to low-slung learning cost. However, the computational power of CNN, is that it cannot provide low-slung learning cost. On the other hand, Quantum Neural Network (QNN) can be a good computational network instead of CNN approaches. In this paper, we present a new computational approach for dynamic ontologies evaluation to the Quantum Perceptron Neural Network (QPNN) can achieve low-slung learning cost. The proposed Quantum Neural Network can construct self-adaptive activation operators that have the capability to accomplish the learning process in a limited number of iterations and thereby, reduce the overall computational cost. The proposed approach is capable to construct its own set of activation operators to be applied widely in both quantum and classical applications to overcome the linearity limitation of classical perceptron.