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CORRECTION article

Front. Big Data, 10 June 2020
Sec. Data Mining and Management

Corrigendum: FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques

  • 1Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO, United States
  • 2Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO, United States
  • 3Department of Health Management and Informatics, University of Missouri-Columbia, Columbia, MO, United States

A Corrigendum on
FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques

Gharibi, M., Zachariah, A., and Rao, P. (2020). Front. Big Data 3:12. doi: 10.3389/fdata.2020.00012

In the published article, there was an error in the affiliation of the first author. Instead of the “University of Missouri-Columbia,” the university name should be “University of Missouri-Kansas City.”

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Keywords: machine learning, graph embeddings, knowledge graphs, AGROVOC, semantic similarity

Citation: Gharibi M, Zachariah A and Rao P (2020) Corrigendum: FoodKG: A Tool to Enrich Knowledge Graphs Using Machine Learning Techniques. Front. Big Data 3:21. doi: 10.3389/fdata.2020.00021

Received: 01 May 2020; Accepted: 12 May 2020;
Published: 10 June 2020.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2020 Gharibi, Zachariah and Rao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Mohamed Gharibi, mggvf@mail.umkc.edu

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.