In this work, we present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning. To this end, we introduce a new model for statistical relational learning that is built upon deep recursive neural networks, and give experimental evidence that it can easily compete with, or even outperform, existing logic-based reasoners on the task of ontology reasoning. More precisely, we compared our implemented system with one of the best logic-based ontology reasoners at present, RDFox, on a number of large standard benchmark datasets, and found that our system attained high reasoning quality, while being up to two orders of magnitude faster.
@article{arxiv.1705.10342,
title = {Deep Learning for Ontology Reasoning},
author = {Patrick Hohenecker and Thomas Lukasiewicz},
journal= {arXiv preprint arXiv:1705.10342},
year = {2017}
}