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.

本研究提出了一种新颖的用于关系表示学习的框架，该框架结合了传统的一阶逻辑和自动编码原理，使用逻辑程序而非神经网络进行原始表示和潜在表示之间的映射，将学习视为约束优化问题，得到的潜在表示在关系学习任务中表现优异，相较于深度学习方法，该框架更为准确、可解释性更强、更具灵活性。