This paper investigates the possibilities for post-processing results of association rule mining algorithms with topic maps. Converting discovered association rules (DARs) as well as background knowledge to a topic map representation allows to assess the interestingness of discovered rules automatically with a topic map query language. This paper introduces a DAR ontology based on the GUHA method, a background knowledge ontology and a way of linking these two ontologies. It is shown on an example how these topic map ontologies can be used to represent particular mining data and how the tolog query language can be used to automatically find interesting rules in such a representation.