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

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 structure...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2023-01, Vol.35 (1), p.614-633
Main Authors: von Rueden, Laura, Mayer, Sebastian, Beckh, Katharina, Georgiev, Bogdan, Giesselbach, Sven, Heese, Raoul, Kirsch, Birgit, Pfrommer, Julius, Pick, Annika, Ramamurthy, Rajkumar, Walczak, Michal, Garcke, Jochen, Bauckhage, Christian, Schuecker, Jannis
Format: Article
Language:English
Subjects:
expert knowledge
hybrid
informed
Knowledge representation
Machine learning
Mathematical model
neuro-symbolic
Pipelines
prior knowledge
survey
Systematics
Taxonomy
Training
Training data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2021.3079836