The paper describes a methodology for explaining results of other data mining methods. We used an existing semantic subgroup discovery algorithm that takes as input groups of examples (the output of other methods) and a domain ontology. The output of the algorithm is a set of descriptive rules that describe and explain the differences between the input sets with conjunctions of ontological concepts. In the paper we used the publicly available Gene Ontology (GO) together with breast cancer experimental data (gene expression profiles). The ontology encodes biological domain knowledge about cells of various organisms. In the first part of the experiment we employed subgroup discovery to get rules in terms of genes. These rules represent the low-level description of the data. In the second part of the experiment we then employed semantic subgroup discovery to infer high-level explanations of the rules inferred in the first part. These high-level descriptions were constructed from the biological knowledge encoded in GO and offer a useful and different view of the same data. The experimental workflow was implemented as an executable workflow in the ClowdFlows platform. The workflow is publicly accessible at: http://clowdflows.org/workflow/911/.
COBISS.SI-ID: 27322407