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One of the most difficult obstacles in the practical application of probabilistic methods is the effort that is required for model building and, in particular, for quantifying graphical models with numerical probabilities. The construction of Bayesian Networks (BNs) with the help of human experts is a difficult and time consuming task, which is prone to errors and omissions especially when the problems are very complicated or there are numerous variables involved. Learning the structure of a BN model and causal relations from a dataset or database is important for extensive BNs analysis. In general, the causal structure and the numerical parameters of a BN can be obtained using two distinct approaches. First, they can be obtained from an expert. Second, they can also be learned from a data set. The main drawback of the first approach is that sometimes there is not enough causal knowledge to establish the structure of the network model with certainty and estimation of probabilities required for a typical application is a time-consuming task because of the number of parameters required (typically hundreds or even thousands of values). Thus, the second approach can initially help human experts build a BN model and they can make it applicable at a later time. In practice, some combination of these two approaches is typically used. This paper essentially focuses on using the second approach. This paper presents a practical framework for automating the building of diagnostic BN models from data sources obtained from the WWW and demonstrates the use of a SMILE web-based interface to represent them. This work proposes the following components: 1) an RSS agent that automatically gathers RSS feeds from diverse data sources in the WWW environment, 2) a transformation/conversion tool that transforms and converts the collected data for both continuous and discrete valued data sets 3) a reasoning engine that has the ability to learn and build the causal structure for BN models from data and provide functionality to perform a diagnosis, 4) the visualization of BN models on a website, and 5) a diagnosis of the BN model and the resulting reports. This article is organized as follows: Section 2 presents a little more detail about the basic concepts of Bayesian networks and tools. Section 3 addresses related work. Section 4 describes the design and implementation of a practical framework for automating the building of diagnostic BN models from online

Automatically Building Diagnostic Bayesian Networks from On-line Data Sources and the SMILE Web-based Interface