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

## TL;DR

This paper provides a comprehensive taxonomy and survey of methods for integrating prior knowledge into machine learning systems to overcome data limitations, categorizing approaches by knowledge source, representation, and integration techniques.

## Contribution

It introduces a formal definition and taxonomy for informed machine learning, systematically classifies existing approaches, and analyzes their use of various knowledge representations.

## Key findings

- Key methods identified through taxonomy classification
- Knowledge integration improves learning with limited data
- Survey covers algebraic, logical, and simulation-based knowledge

## Abstract

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.