Semantic Bayesian Network

  • Monidipa DasEmail author
  • Soumya K. Ghosh
Part of the Studies in Computational Intelligence book series (SCI, volume 858)


In spite of the fact that BN is inherently capable of representing, learning, and reasoning with uncertain knowledge , the performance of BN highly depends on the size of available training dataset. A proper learning of the network needs large amount of observed data be available during the training procedure. Otherwise, it may result in strongly biased inference due to parameter learning uncertainty . The recent research indicates that a prior knowledge about the respective domain  may help in reducing such epistemic uncertainty . This chapter discusses on semBnet, a semantically enhanced Bayesian network model, which has inherent capability of incorporating external knowledge over the associated domain. Consequently, semBnet is potential enough to model spatio-temporal relationships  in an improved manner with incorporated domain knowledge . The performance of semBnet-based prediction model has been evaluated in comparison with a number of conventional statistical and state-of-the-art space-time prediction models, with respect to a case study on climatological time series data . Experimental result demonstrates the superiority of semBnet over the other models considered.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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