# Data Science with Vadalog: Bridging Machine Learning and Reasoning

arXiv: https://arxiv.org/abs/1807.08712 · HTML: https://mlsanity.com/paper/1807.08712/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Harmless but Useful: Beyond Separable Equality Constraints in Datalog+/-](https://mlsanity.com/paper/2105.11147/) (2021)
- [Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2210.00283/) (2022)

## Abstract (arXiv)

Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modelling, and systems for reasoning with domain knowledge. In this paper we present a state-of-the-art Knowledge Graph Management System, Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits, such as the Jupyter platform. We demonstrate how to use Vadalog to perform traditional data wrangling tasks, as well as complex logical and probabilistic reasoning. We argue that this is a significant step forward towards combining machine learning and reasoning in data science.