Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences
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 inconsiste...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2021.660101/full |
| _version_ | 1818595469748600832 |
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| author | Jon Haël Brenas Arash Shaban-Nejad |
| author_facet | Jon Haël Brenas Arash Shaban-Nejad |
| author_sort | Jon Haël Brenas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T11:16:31Z |
| format | Article |
| id | doaj.art-e1754c2c8e6840e5b79e6b774900d8f7 |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-12-16T11:16:31Z |
| publishDate | 2021-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-e1754c2c8e6840e5b79e6b774900d8f72022-12-21T22:33:35ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2021-05-01410.3389/fdata.2021.660101660101Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood ExperiencesJon Haël Brenas0Arash Shaban-Nejad1Nuffield Department of Public Health, Big Data Institute, University of Oxford, Oxford, United KingdomDepartment of Pediatrics, The University of Tennessee Health Science Center-Oak Ridge National Laboratory, Center for Biomedical Informatics, College of Medicine, Memphis, TN, United StatesKnowledge 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.https://www.frontiersin.org/articles/10.3389/fdata.2021.660101/fullprogram verificationgraph transformationcloningmergingknowledge graphadverse childhood experiences |
| spellingShingle | Jon Haël Brenas Arash Shaban-Nejad Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences Frontiers in Big Data program verification graph transformation cloning merging knowledge graph adverse childhood experiences |
| title | Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences |
| title_full | Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences |
| title_fullStr | Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences |
| title_full_unstemmed | Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences |
| title_short | Proving the Correctness of Knowledge Graph Update: A Scenario From Surveillance of Adverse Childhood Experiences |
| title_sort | proving the correctness of knowledge graph update a scenario from surveillance of adverse childhood experiences |
| topic | program verification graph transformation cloning merging knowledge graph adverse childhood experiences |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2021.660101/full |
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