Learning Relational Representations with Auto-encoding Logic Programs
This addresses the need for more accurate and interpretable relational representation learning, though it is incremental in combining existing symbolic and deep learning approaches.
The paper tackles the problem of representing relational data by introducing a framework that uses first-order logic and logic programs for auto-encoding, combining the accuracy and interpretability of symbolic methods with the scalability of deep learning, and shows experimentally that these latent representations improve relational learning tasks.
Deep learning methods capable of handling relational data have proliferated over the last years. In contrast to traditional relational learning methods that leverage first-order logic for representing such data, these deep learning methods aim at re-representing symbolic relational data in Euclidean spaces. They offer better scalability, but can only numerically approximate relational structures and are less flexible in terms of reasoning tasks supported. This paper introduces a novel framework for relational representation learning that combines the best of both worlds. This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the original and latent representation is done by means of logic programs instead of neural networks. We show how learning can be cast as a constraint optimisation problem for which existing solvers can be used. The use of logic as a representation language makes the proposed framework more accurate (as the representation is exact, rather than approximate), more flexible, and more interpretable than deep learning methods. We experimentally show that these latent representations are indeed beneficial in relational learning tasks.