FOIL-D: Efficiently Scaling FOIL for Multi-relational Data Mining of Large Datasets

Multi-relational rule mining is important for knowledge discovery in relational databases as it allows for discovery of patterns involving multiple relational tables. Inductive logic programming (ILP) techniques have had considerable success on a variety of multi-relational rule mining tasks, however, most ILP systems do not scale to very large datasets. In this paper we present two extensions to a popular ILP system, FOIL, that improve its scalability. (i) We show how to interface FOIL directly to a relational database management system. This enables FOIL to run on data sets that previously had been out of its scope. (ii) We describe estimation methods, based on histograms, that significantly decrease the computational cost of learning a set of rules. We present experimental results that indicate that on a set of standard ILP datasets, the rule sets learned using our extensions are equivalent to those learned with standard FOIL but at considerably less cost.