01430nas a2200133   4500008004100000245008200041210006900123260000900192520091700201100002301118700002401141700001901165856011201184       2016                        eng d00aClassification Rule Mining Supported by Ontology for Discrimination Discovery0 aClassification Rule Mining Supported by Ontology for Discriminat  bIEEE3 aDiscrimination discovery from data consists of designing data mining methods for the actual discovery of discriminatory situations and practices hidden in a large amount of historical decision records. Approaches based on classification rule mining consider items at a flat concept level, with no exploitation of background knowledge on the hierarchical and inter-relational structure of domains. On the other hand, ontologies are a widespread and ever increasing means for expressing such a knowledge. In this paper, we propose a framework for discrimination discovery from ontologies, where contexts of prima-facie evidence of discrimination are summarized in the form of generalized classification rules at different levels of abstraction. Throughout the paper, we adopt a motivating and intriguing case study based on discriminatory tariffs applied by the U. S. Harmonized Tariff Schedules on imported goods.1 aLuong, Binh, Thanh1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/classification-rule-mining-supported-ontology-discrimination-discovery