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• W3214716715 abstract "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 modeling, 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." @default.
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• W3214716715 date "2022-04-01" @default.
• W3214716715 modified "2023-10-16" @default.
• W3214716715 title "Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice" @default.
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• W3214716715 doi "https://doi.org/10.1016/j.future.2021.10.021" @default.
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