Data Science with Vadalog: Bridging Machine Learning and Reasoning

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 th...

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Bibliographic Details
Published in:arXiv.org 2018-07
Main Authors: Bellomarini, Luigi, Fayzrakhmanov, Ruslan R, Gottlob, Georg, Kravchenko, Andrey, Laurenza, Eleonora, Nenov, Yavor, Reissfelder, Stephane, Sallinger, Emanuel, Sherkhonov, Evgeny, Wu, Lianlong
Format: Article
Language:English
Subjects:
Artificial intelligence
Cognition & reasoning
Data science
Machine learning
Reasoning
State of the art
Statistical analysis
Statistical models
Task complexity
Toolkits
Online Access:Get full text
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Summary: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.
ISSN:2331-8422