  We propose a new method for mining frequent patterns in a language that
combines both Semantic Web ontologies and rules. In particular we consider the
setting of using a language that combines description logics with DL-safe
rules. This setting is important for the practical application of data mining
to the Semantic Web. We focus on the relation of the semantics of the
representation formalism to the task of frequent pattern discovery, and for the
core of our method, we propose an algorithm that exploits the semantics of the
combined knowledge base. We have developed a proof-of-concept data mining
implementation of this. Using this we have empirically shown that using the
combined knowledge base to perform semantic tests can make data mining faster
by pruning useless candidate patterns before their evaluation. We have also
shown that the quality of the set of patterns produced may be improved: the
patterns are more compact, and there are fewer patterns. We conclude that
exploiting the semantics of a chosen representation formalism is key to the
design and application of (onto-)relational frequent pattern discovery methods.
Note: To appear in Theory and Practice of Logic Programming (TPLP)
