  The ability to conduct logical reasoning is a fundamental aspect of
intelligent human behavior, and thus an important problem along the way to
human-level artificial intelligence. Traditionally, logic-based symbolic
methods from the field of knowledge representation and reasoning have been used
to equip agents with capabilities that resemble human logical reasoning
qualities. More recently, however, there has been an increasing interest in
using machine learning rather than logic-based symbolic formalisms to tackle
these tasks. In this paper, we employ state-of-the-art methods for training
deep neural networks to devise a novel model that is able to learn how to
effectively perform logical reasoning in the form of basic ontology reasoning.
This is an important and at the same time very natural logical reasoning task,
which is why the presented approach is applicable to a plethora of important
real-world problems. We present the outcomes of several experiments, which show
that our model is able to learn to perform highly accurate ontology reasoning
on very large, diverse, and challenging benchmarks. Furthermore, it turned out
that the suggested approach suffers much less from different obstacles that
prohibit logic-based symbolic reasoning, and, at the same time, is surprisingly
plausible from a biological point of view.
