Authors: Akihiro Takemura, Katsumi Inoue
Abstract: We introduce a new method for integrating neural networks with logic
programming in Neural-Symbolic AI (NeSy), aimed at learning with distant
supervision, in which direct labels are unavailable. Unlike prior methods, our
approach does not depend on symbolic solvers for reasoning about missing
labels. Instead, it evaluates logical implications and constraints in a
differentiable manner by embedding both neural network outputs and logic
programs into matrices. This method facilitates more efficient learning under
distant supervision. We evaluated our approach against existing methods while
maintaining a constant volume of training data. The findings indicate that our
method not only matches or exceeds the accuracy of other methods across various
tasks but also speeds up the learning process. These results highlight the
potential of our approach to enhance both accuracy and learning efficiency in
NeSy applications.
Source: http://arxiv.org/abs/2408.12591v1
