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.
Vadalog: Recent Advances and Applications
TLDR
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
TLDR
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
TLDR
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.
Harmless but Useful: Beyond Separable Equality Constraints in Datalog+/-
TLDR
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.

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