Abstract: Relational learning is becoming a focus in machine learning research. In relational learning, samples cannot be represented as vectors in R\+n. This characteristic distinguishes it from other machinelearning tasks in that relational learning cannot utilize the geometric structure in R\+n and thus is much harder to solve. In this paper a multiple kernel learning approach for relational learning is proposed. First of all, it is proved that for multiple kernel learning with the kernels induced by logical rules, it suffices to use the linear kernel. Based on this, through iteratively constructing rules by a modified FOIL algorithm and performing the corresponding multiple kernel optimization, the proposed approach realizes an additive model on the feature space induced by the obtained rules. The algorithm is characterized by its “bi-sparsity”, i.e., support rules and support vectors are obtained simultaneously. Moreover, it is proved that the multiple kernel learning in the feature space induced by rules is equivalent to squared \-1 SVM. The proposed algorithm is evaluated on six real world datasets from bioinformatics and chemoinformatics. Experimental results demonstrate that the approach has better prediction accuracy than previous approaches. Meanwhile, the output classifier has a straightforward interpretation and relies on a smaller number of rules.