Deep Learning for Ontology Reasoning
This addresses the problem of slow reasoning speeds in ontology systems for AI and knowledge management, representing a novel method rather than an incremental improvement.
The paper tackled ontology reasoning by introducing a deep learning-based model using deep recursive neural networks, achieving reasoning quality comparable to the state-of-the-art logic-based reasoner RDFox while being up to two orders of magnitude faster on standard benchmarks.
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.