Title: RelNN: A Deep Neural Model for Relational Learning,
Abstract: Statistical relational AI (StarAI) aims at reasoning and learning in noisy
domains described in terms of objects and relationships by combining
probability with first-order logic. With huge advances in deep learning in the
current years, combining deep networks with first-order logic has been the
focus of several recent studies. Many of the existing attempts, however, only
focus on relations and ignore object properties. The attempts that do consider
object properties are limited in terms of modelling power or scalability. In
this paper, we develop relational neural networks (RelNNs) by adding hidden
layers to relational logistic regression (the relational counterpart of
logistic regression). We learn latent properties for objects both directly and
through general rules. Back-propagation is used for training these models. A
modular, layer-wise architecture facilitates utilizing the techniques developed
within deep learning community to our architecture. Initial experiments on
eight tasks over three real-world datasets show that RelNNs are promising
models for relational learning.