# A Vision for Semantically Enriched Data Science

arXiv: https://arxiv.org/abs/2303.01378 · HTML: https://mlsanity.com/paper/2303.01378/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## 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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Model-Agnostic Counterfactual Explanations for Consequential Decisions](https://mlsanity.com/paper/1905.11190/) (2019)
- [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)
- [Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals](https://mlsanity.com/paper/2103.11972/) (2021)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach](https://mlsanity.com/paper/2010.13273/) (2020)
- [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)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (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)
- [Mining Documentation to Extract Hyperparameter Schemas](https://mlsanity.com/paper/2006.16984/) (2020)

## Abstract (arXiv)

The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are areas where we have seen little automation. Data Scientists have long leveraged common sense reasoning and domain knowledge to understand and enrich data for building predictive models. In this paper we discuss important shortcomings of current data science and machine learning solutions. We then envision how leveraging "semantic" understanding and reasoning on data in combination with novel tools for data science automation can help with consistent and explainable data augmentation and transformation. Additionally, we discuss how semantics can assist data scientists in a new manner by helping with challenges related to trust, bias, and explainability in machine learning. Semantic annotation can also help better explore and organize large data sources.