  We investigate the problem of extracting rules, expressed in Horn logic, from
neural network models. Our work is based on the exact learning model, in which
a learner interacts with a teacher (the neural network model) via queries in
order to learn an abstract target concept, which in our case is a set of Horn
rules. We consider partial interpretations to formulate the queries. These can
be understood as a representation of the world where part of the knowledge
regarding the truthiness of propositions is unknown. We employ Angluin s
algorithm for learning Horn rules via queries and evaluate our strategy
empirically.
