Deep Learning for Ontology Reasoning

By Patrick Hohenecker, Thomas Lukasiewicz. 2017

35 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “Ontology reasoning, nowadays based on deep learning rather than logic-based formal reasoning (Hohenecker and Lukasiewicz, 2017) is of great interest, and the applications of ontology-guided approaches are of much practical help for the medical domain expert (Girardi et al., 2016; Wartner et al.,…”
Deep learning for noise-tolerant RDFS reasoning (2019, background): “One of the closest research efforts to the scope of this research is [93].”