Ontology-driven Bayesian Network Model for Semantic Expression

Abstract

In this paper, ontology-driven Bayesian network model is proposed using the semantic ontology knowledge base, which automatically transforms the entities of the ontology into the Bayesian network model. Brief detail of the advantage of Bayesian network is applied for solving the uncertain and non-complete information. The hypertensive ontology is constructed to prove the validity of the model. The medical diagnosis algorithm based on the ontology-driven Bayesian network model to assist the NETICA Application Programming Interface (API). The proposed model used to realize the mapping between ontology and Bayesian network, and the different probability of the condition is entered to obtain the probability that the patient is suffering from high blood pressure. The experimental results show that the model is correct and feasible, and it has good universality and portability in medical diagnosis.