The article introduces a new method to learn about relationships between hidden and observed variables from data. By using a technique called principal component analysis, the method identifies hidden variables from observed data. Then, it constructs a structure of relationships between these hidden variables using Bayesian networks. The researchers tested this method with artificial data and found that it works effectively in extracting hidden variables and their relationships from the data.