Using Bayesian classifiers to combine rules

1 Introduction The last few years have seen a surge of interest in multi-relational data mining,with applications in areas as diverse as bioinformatics and link discovery. One of the most popular techniques for multi-relational data mining is InductiveLogic Programming (ILP). Given a set of positive and negative examples, an ILP system ideally finds a logical description of the underlying data model thatdifferentiates between the positive and negative examples. ILP systems confer the advantages of a solid mathematical foundation and the ability to generateunderstandable explanations. As ILP systems are being applied to tasks of increasing difficulty, issues suchas large search spaces and erroneous or missing data have become more relevant. Ultimately, ILP systems can only expect to search a relatively modest number ofclauses, usually on the order of millions. Evaluating increasingly complex clauses may not be the solution. As clauses grow larger, they become more vulnerableto the following errors: a query will fail because of missing data, a query will encounter an erroneous database item, and a clause will give correct answerssimply by chance