@conference {968,
	title = {Classification Rule Mining Supported by Ontology for Discrimination Discovery},
	booktitle = {Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {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.},
	doi = {10.1109/ICDMW.2016.0128},
	author = {Luong, Binh Thanh and Salvatore Ruggieri and Franco Turini}
}
