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Join Bayes Nets: A new type of Bayes net for relational data

Many real-world data are maintained in relational format, with different tables storing information about entities and their links or relationships. The structure (schema) of the database is essentially that of a logical language, with variables ranging over individual entities and predicates for relationships and attributes. Our work combines the graphical structure of Bayes nets with the logical structure of relational databases to achieve knowledge discovery in databases. We introduce Join Bayes nets, a new type of Bayes nets for representing and learning class-level dependencies between attributes from the same table and from different tables; such dependencies are important for policy making and strategic planning. Focusing on class-level dependencies brings advantages in terms of the simplicity of the model and the tractability of inference and learning. As usual with Bayes nets, the graphical structure supports efficient inference and reasoning. We show that applying standard Bayes net inference algorithms to the learned models provides fast and accurate probability estimates for queries that involve attributes and relationships from multiple tables.