A Survey on Semantics in Automated Data Science
This is a survey paper discussing incremental improvements for data scientists by addressing semantic gaps in automation.
The paper identifies shortcomings in automated data science, particularly in feature engineering, and proposes that integrating semantic reasoning can improve data augmentation, transformation, trust, bias, and explainability.
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models. In recent years, we have witnessed a surge in tools and techniques for {\em automated machine learning}. While data scientists can employ various such tools to help with model building, many other aspects such as {\em feature engineering} that require semantic understanding of concepts, remain manual to a large extent. In this paper we discuss important shortcomings of current automated data science solutions and machine learning. We discuss how leveraging basic semantic reasoning on data in combination with novel tools for data science automation can help with consistent and explainable data augmentation and transformation. Moreover, semantics can assist data scientists in a new manner by helping with challenges related to {\em trust}, {\em bias}, and {\em explainability}.