Knowledge Enhanced Neural Networks for relational domains

TL;DR

This paper introduces an extension of KENN, a neural-symbolic framework, for relational data, demonstrating improved performance and scalability by integrating logical knowledge into neural networks.

Contribution

The paper extends KENN to relational domains, enhancing scalability and robustness, and shows its effectiveness through experimental validation.

Findings

KENN improves neural network accuracy with logical knowledge.

The extended KENN handles relational data efficiently.

It requires less training time compared to similar methods.

Abstract

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 this work we focus on a specific method, KENN (Knowledge Enhanced Neural Networks), a Neural-Symbolic architecture that injects prior logical knowledge into a neural network by adding on its top a residual layer that 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 rule 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. One of the main advantages of KENN resides in its scalability, thanks to a flexible treatment of dependencies between the rules obtained by stacking multiple logical layers. We show experimentally the efficacy of this strategy. The results show that KENN is capable of increasing the performances of the underlying neural network, obtaining better or comparable accuracies in respect to other two related methods that combine learning with logic, requiring significantly less time for learning.