13,778 research outputs found
Ontology Reasoning with Deep Neural Networks
The ability to conduct logical reasoning is a fundamental aspect of
intelligent behavior, and thus an important problem along the way to
human-level artificial intelligence. Traditionally, symbolic logic-based
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 symbolic logic-based 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 learned to perform precise ontology reasoning on diverse and
challenging tasks. 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
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
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