A Roadmap to Domain Knowledge Integration in Machine Learning

TL;DR

This paper reviews various methods of integrating domain knowledge into machine learning models to improve performance despite data limitations, highlighting different knowledge representations and their effectiveness.

Contribution

It provides a comprehensive overview of knowledge integration techniques and evaluates their performance across various machine learning tasks.

Findings

Different knowledge representations impact model performance variably.

Knowledge integration can mitigate data scarcity issues.

The paper identifies key challenges and future directions in knowledge-based ML.

Abstract

Many machine learning algorithms have been developed in recent years to enhance the performance of a model in different aspects of artificial intelligence. But the problem persists due to inadequate data and resources. Integrating knowledge in a machine learning model can help to overcome these obstacles up to a certain degree. Incorporating knowledge is a complex task though because of various forms of knowledge representation. In this paper, we will give a brief overview of these different forms of knowledge integration and their performance in certain machine learning tasks.