Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Ontology Reasoning   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.

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The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
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The categories of this paper are: eess
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ