Knowledge Enhanced Neural Networks

We propose Knowledge Enhanced Neural Networks (KENN), an architecture for injecting prior knowledge, codified by a set of logical clauses, into a neural network. In KENN clauses are directly incorporated in the structure of the neural network as a new layer that includes a set of additional learnable parameters, called clause weights. As a consequence, KENN can learn the level of satisfiability to impose in the final classification. When training data contradicts a constraint, KENN learns to ignore it, making the system robust to the presence of wrong knowledge. Moreover, the method returns learned clause weights, which gives us informations about the influence of each constraint in the final predictions, increasing the interpretability of the model. We evaluated KENN on two standard datasets for multi-label classification, showing that the injection of clauses automatically extracted from the training data sensibly improves the performances. Furthermore, we apply KENN to solve the problem of finding relationship between detected objects in images by adopting manually curated clauses. The evaluation shows that KENN outperforms the state of the art methods on this task.