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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

: Rueden, Laura von; Mayer, Sebastian; Beckh, Katharina; Georgiev, Bogdan; Giesselbach, Sven; Heese, Raoul; Kirsch, Birgit; Walczak, Michal; Pfrommer, Julius; Pick, Annika; Ramamurthy, Rajkumar; Garcke, Jochen; Bauckhage, Christian; Schuecker, Jannis

Volltext urn:nbn:de:0011-n-6363311 (1.2 MByte PDF)
MD5 Fingerprint: 7592b41aacad1360a6b15616b2de959d
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Erstellt am: 30.6.2021

IEEE transactions on knowledge and data engineering (2021), Online First, 20 S.
ISSN: 1041-4347
ISSN: 1558-2191
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IOSB ()
machine learning; prior knowledge; expert knowledge; Informed; hybrid; Neuro-Symbolic; survey; Taxonomy

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.