  In the recent past, there has been a growing interest in Neural-Symbolic
Integration frameworks, i.e., hybrid systems that integrate connectionist and
symbolic approaches to obtain the best of both worlds. In a previous work, we
proposed KENN (Knowledge Enhanced Neural Networks), a Neural-Symbolic
architecture that injects prior logical knowledge into a neural network by
adding a new final layer which modifies the initial predictions accordingly to
the knowledge. Among the advantages of this strategy, there is the inclusion of
clause weights, learnable parameters that represent the strength of the
clauses, meaning that the model can learn the impact of each clause on the
final predictions. As a special case, if the training data contradicts a
constraint, KENN learns to ignore it, making the system robust to the presence
of wrong knowledge. In this paper, we propose an extension of KENN for
relational data. To evaluate this new extension, we tested it with different
learning configurations on Citeseer, a standard dataset for Collective
Classification. The results show that KENN is capable of increasing the
performances of the underlying neural network even in the presence relational
data, outperforming other two notable methods that combine learning with logic.
