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, SwitzerlandCopyright © 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