Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice
| Autor: | Eleonora Laurenza, Lianlong Wu, Ruslan R. Fayzrakhmanov, Andrey Kravchenko, Sahar Vahdati, Luigi Bellomarini, Georg Gottlob, Emanuel Sallinger, Stéphane Reissfelder, Evgeny Sherkhonov, Yavor Nenov |
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| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Computer science Logical reasoning business.industry Probabilistic logic Statistical model Base (topology) Machine learning computer.software_genre Data science Workflow Hardware and Architecture Logical conjunction Management system Domain knowledge Artificial intelligence business computer Software |
| Zdroj: | Future Generation Computer Systems. 129:407-422 |
| ISSN: | 0167-739X |
| Popis: | 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 demonstrate how to perform a broad spectrum of data science tasks in a unified Knowledge Graph environment. This includes data wrangling, complex logical and probabilistic reasoning, and machine learning. We base our work on the 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 argue that this is a significant step forward towards practical, holistic data science workflows that combine machine learning and reasoning in data science. |
| Databáze: | OpenAIRE |
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