@article{VonRueden.ea:2021,
abstract = {Despite the great successes of machine learning, it can have its limits when dealing with insufficient training data.A potential solution is to incorporate additional knowledge into the training process which leads to the idea of informed machine learning. We present a research survey and structured overview of various approaches in this field. We aim to establish a taxonomy which can serve as a classification framework that considers the kind of additional knowledge, its representation,and its integration into the machine learning pipeline. The evaluation of numerous papers on the bases of the taxonomy uncovers key methods in this field.},
archivePrefix = {arXiv},
arxivId = {1903.12394},
author = {von Rueden, Laura and Mayer, Sebastian and Katharina Beckh and Bogdan Georgiev and Sven Giesselbach and Raoul Heese and Birgit Kirsch and Julius Pfrommer and Annika Pick and Rajkumar Ramamurthy and Michał Walczak and Garcke, Jochen and Bauckhage, Christian and Schuecker, Jannis},
eprint = {1903.12394},
title = {Informed Machine Learning - {A} Taxonomy and Survey of Integrating Knowledge into Learning Systems},
url = {http://arxiv.org/abs/1903.12394},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = {2023},
doi = {10.1109/TKDE.2021.3079836},
volume = 35,
number = 1,
pages = {614-633}
}