Multi-Relational Data Mining using Probabilistic Models Research Summary

We are often faced with the challenge of mining data represented in relational form. Unfortunately, most statistical learning methods work only with “flat” data representations. Thus, to apply these methods, we are forced to convert the data into a flat form, thereby not only losing its compact representation and structure but also potentially introducing statistical skew. These drawbacks severely limit the ability of current statistical methods to mine relational databases. Probabilistic models, in particular probabilistic relational models, allow us to represent a statistical model over a relational domain. These models can represent correlations between attributes within a single table, and between attributes in multiple tables, when these tables are related via foreign key joins. In previous work [4, 6, 8], we have developed algorithms for automatically constructing a probabilistic relational model directly from a relational database. We survey the results here and describe how the methods can be used to discover interesting dependencies the data. We show how this class of models and our construction algorithm are ideally suited to mining multi-relational data.