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Front Big Data. 2021 May 03;4:660101. doi: 10.3389/fdata.2021.660101. eCollection 2021.

Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences.

Frontiers in big data

Jon Haël Brenas, Arash Shaban-Nejad

Affiliations

  1. Nuffield Department of Public Health, Big Data Institute, University of Oxford, Oxford, United Kingdom.
  2. Department of Pediatrics, The University of Tennessee Health Science Center-Oak Ridge National Laboratory, Center for Biomedical Informatics, College of Medicine, Memphis, TN, United States.

PMID: 34013202 PMCID: PMC8126660 DOI: 10.3389/fdata.2021.660101

Abstract

Knowledge graphs are a modern way to store information. However, the knowledge they contain is not static. Instances of various classes may be added or deleted and the semantic relationship between elements might evolve as well. When such changes take place, a knowledge graph might become inconsistent and the knowledge it conveys meaningless. In order to ensure the consistency and coherency of dynamic knowledge graphs, we propose a method to model the transformations that a knowledge graph goes through and to prove that the new transformations do not yield inconsistencies. To do so, we express the knowledge graphs as logically decorated graphs, then we describe the transformations as algorithmic graph transformations and we use a Hoare-like verification process to prove correctness. To demonstrate the proposed method in action, we use examples from Adverse Childhood Experiences (ACEs), which is a public health crisis.

Copyright © 2021 Brenas and Shaban-Nejad.

Keywords: adverse childhood experiences; cloning; graph transformation; knowledge graph; merging; program verification

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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