Abstract. A novel family of parametric language-independent kernel functions defined for individuals within ontologies is presented. They are easily integrated with efficient statistical learning methods for inducing linear classifiers that offer an alternative way to perform classification w.r.t. deductive reasoning. A method for adapting the parameters of the kernel to the knowledge base through stochastic optimization is also proposed. This enables the exploitation of statistical learning in a variety of tasks where an inductive approach may bridge the gaps of the standard methods due the inherent incompleteness of the knowledge bases. In this work, a system integrating the kernels has been tested in experiments on approximate query answering with real ontologies collected from standard repositories. In the context of the Semantic Web (henceforth SW) many applications require the accomplishment of data-intensive tasks that can effectively exploit machine learning methods [1] . However, while a growing amount of metadata is being produced, most of the research effort addresses the problem of learning for the SW (mostly from structured or unstructured text [2]). Less attention was devoted to the advantages (and problems) of learning from SW data and metadata expressed in Description Logics (DLs) [3] . Classification is a central task for many applications. However, classifying through logic reasoning may be both too demanding because of its complexity and also too weak because of inconsistency or (inherent) incompleteness in the knowledge bases [4] . So far, for the sake of tractability, only simple DL languages have been considered in the development of logic-based learning methods [5, 6] . On the other end, efficient machine learning methods, that were originally developed for simple data, can be effectively upgraded to work with richer structured representations [7] . These methods have been shown to effectively solve unsupervised and supervised learning problems in DLs [8, 9] , particularly those based on classification, clustering and ranking of individuals. Although the inductive methods that will be presented are general and could in principle be exploited in various scenarios, we will focus on methods for inducing efficient classifiers from examples and use them to carry out forms of approximate query answering (and concept retrieval). This task is normally performed by recurring to standard deductive reasoning procedures [3] . Hence it may turn out to be ineffective when (inconsistent or) incomplete knowledge is available, which is not infrequent with heterogeneous and distributed data sources.
An interesting related work on statistical learning for query answering is in REF .
Statistical learning for inductive query answering on owl ontologies
In Proceedings of the 7th International Semantic Web Conference (ISWC
Computer Science