# Data Science with Vadalog: Bridging Machine Learning and Reasoning

@article{Bellomarini2018DataSW, title={Data Science with Vadalog: Bridging Machine Learning and Reasoning}, author={Luigi Bellomarini and Ruslan R. Fayzrakhmanov and Georg Gottlob and Andrey Kravchenko and Eleonora Laurenza and Yavor Nenov and St{\'e}phane Reissfelder and Emanuel Sallinger and Evgeny Sherkhonov and Lianlong Wu}, journal={ArXiv}, year={2018}, volume={abs/1807.08712} }

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. [... ] Key Result We argue that this is a significant step forward towards combining machine learning and reasoning in data science. Expand

## 6 Citations

Vadalog: Recent Advances and Applications

- Computer ScienceJELIA
- 2019

An easy-to-access self-contained introduction to Warded Datalog+/−, the logical core of Vadalog, is given, and some recent practical applications of the Vad analog language are presented: detection of close links in financial knowledge graphs, as well as the detection of family-owned businesses.

Knowledge Graphs and Big Data Processing

- Computer ScienceLecture Notes in Computer Science
- 2020

This introductory chapter serves to characterize the relevant aspects of the Big Data Ecosystem with respect to big data characteristics, the components needed for implementing end-to-end big data processing and the need for using semantics for improving the data management, integration, processing, and analytical tasks.

Weaving Enterprise Knowledge Graphs: The Case of Company Ownership Graphs

- Computer ScienceEDBT
- 2020

An in-depth case analysis of company ownership graphs, graphs having company ownership as a central concept, is presented and Vada-Link, a framework based on state-of-the-art approaches for knowledge representation and reasoning is presented.

Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice

- Computer ScienceFuture Gener. Comput. Syst.
- 2022

Harmless but Useful: Beyond Separable Equality Constraints in Datalog+/-

- Computer ScienceArXiv
- 2021

This paper proposes a more general class of EGDs, which it is called “harmless”, that subsume separable EGDs and allow to model and reason about a much broader class of problems, and contributes a sufficient syntactic condition characterizing harmless EGDs.

Feature Engineering and Explainability with Vadalog: A Recommender Systems Application

- Computer ScienceDatalog
- 2019

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