Ricerc@Sapienza

The rise of data mining and machine learning use in many applications has brought new challenges related to classification. Here, we deal with the following challenge: how to interpret and understand the reason behind a classifier's prediction. Indeed, understanding the behaviour of a classifier is widely recognized as a very important task for wide and safe adoption of machine learning and data mining technologies, especially in high-risk domains, and in dealing with bias.We present a preliminary work on a proposal of using the Ontology-Based Data Management paradigm for explaining the behavior of a classifier in terms of the concepts and the relations that are meaningful in the domain that is relevant for the classifier.