Deep Learning for Ontology Reasoning

Abstract

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.

9 pages

References in corpus (4)

• A Review of Relational Machine Learning for Knowledge Graphs
• PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation
• Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
• Can recursive neural tensor networks learn logical reasoning?

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• From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
• Explainable Deep RDFS Reasoner
• SLD-Resolution Reduction of Second-Order Horn Fragments -- technical report --
• Neural Unification for Logic Reasoning over Natural Language