# A Survey on Semantics in Automated Data Science

arXiv: https://arxiv.org/abs/2205.08018 · HTML: https://mlsanity.com/paper/2205.08018/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 76 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Feature Engineering for Predictive Modeling using Reinforcement Learning](https://mlsanity.com/paper/1709.07150/) (2017)
- [Sherlock: A Deep Learning Approach to Semantic Data Type Detection](https://mlsanity.com/paper/1905.10688/) (2019)
- [Probabilistic Matrix Factorization for Automated Machine Learning](https://mlsanity.com/paper/1705.05355/) (2017)
- [AlphaClean: Automatic Generation of Data Cleaning Pipelines](https://mlsanity.com/paper/1904.11827/) (2019)
- [Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach](https://mlsanity.com/paper/2010.13273/) (2020)
- [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021)
- [Selecting Near-Optimal Learners via Incremental Data Allocation](https://mlsanity.com/paper/1601.00024/) (2015)
- [Semantic Labeling Using a Deep Contextualized Language Model](https://mlsanity.com/paper/2010.16037/) (2020)
- [TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts](https://mlsanity.com/paper/1902.06034/) (2019)
- [SOAR: A Synthesis Approach for Data Science API Refactoring](https://mlsanity.com/paper/2102.06726/) (2021)
- [Semantic Annotation for Tabular Data](https://mlsanity.com/paper/2012.08594/) (2020)
- [Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics](https://mlsanity.com/paper/1908.08005/) (2019)
- [Discovering Mathematical Objects of Interest -- A Study of Mathematical Notations](https://mlsanity.com/paper/2002.02712/) (2020)
- [Recursive Feature Generation for Knowledge-based Learning](https://mlsanity.com/paper/1802.00050/) (2018)

## Abstract (arXiv)

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.