%0 Conference Paper %B Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on %D 2016 %T Classification Rule Mining Supported by Ontology for Discrimination Discovery %A Luong, Binh Thanh %A Salvatore Ruggieri %A Franco Turini %X Discrimination 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. %B Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on %I IEEE %G eng %R 10.1109/ICDMW.2016.0128