Analysis and implementation of the DynDiff tool when comparing versions of ontology

Abstract Background Ontologies play a key role in the management of medical knowledge because they have the properties to support a wide range of knowledge-intensive tasks. The dynamic nature of knowledge requires frequent changes to the ontologies to keep them up-to-date. The challenge is to unders...

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Main Authors: Sara Diaz Benavides, Silvio D. Cardoso, Marcos Da Silveira, Cédric Pruski
Format: Article
Language:English
Published: BMC 2023-09-01
Series:Journal of Biomedical Semantics
Subjects:
Online Access:https://doi.org/10.1186/s13326-023-00295-7
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author Sara Diaz Benavides
Silvio D. Cardoso
Marcos Da Silveira
Cédric Pruski
author_facet Sara Diaz Benavides
Silvio D. Cardoso
Marcos Da Silveira
Cédric Pruski
author_sort Sara Diaz Benavides
collection DOAJ
description Abstract Background Ontologies play a key role in the management of medical knowledge because they have the properties to support a wide range of knowledge-intensive tasks. The dynamic nature of knowledge requires frequent changes to the ontologies to keep them up-to-date. The challenge is to understand and manage these changes and their impact on depending systems well in order to handle the growing volume of data annotated with ontologies and the limited documentation describing the changes. Methods We present a method to detect and characterize the changes occurring between different versions of an ontology together with an ontology of changes entitled DynDiffOnto, designed according to Semantic Web best practices and FAIR principles. We further describe the implementation of the method and the evaluation of the tool with different ontologies from the biomedical domain (i.e. ICD9-CM, MeSH, NCIt, SNOMEDCT, GO, IOBC and CIDO), showing its performance in terms of time execution and capacity to classify ontological changes, compared with other state-of-the-art approaches. Results The experiments show a top-level performance of DynDiff for large ontologies and a good performance for smaller ones, with respect to execution time and capability to identify complex changes. In this paper, we further highlight the impact of ontology matchers on the diff computation and the possibility to parameterize the matcher in DynDiff, enabling the possibility of benefits from state-of-the-art matchers. Conclusion DynDiff is an efficient tool to compute differences between ontology versions and classify these differences according to DynDiffOnto concepts. This work also contributes to a better understanding of ontological changes through DynDiffOnto, which was designed to express the semantics of the changes between versions of an ontology and can be used to document the evolution of an ontology.
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spelling doaj.art-bfff400aaa56444293dc88cd0cda9fbb2023-11-20T11:21:25ZengBMCJournal of Biomedical Semantics2041-14802023-09-0114111910.1186/s13326-023-00295-7Analysis and implementation of the DynDiff tool when comparing versions of ontologySara Diaz Benavides0Silvio D. Cardoso1Marcos Da Silveira2Cédric Pruski3Luxembourg Institute of Science and TechnologyDynaccurateLuxembourg Institute of Science and TechnologyLuxembourg Institute of Science and TechnologyAbstract Background Ontologies play a key role in the management of medical knowledge because they have the properties to support a wide range of knowledge-intensive tasks. The dynamic nature of knowledge requires frequent changes to the ontologies to keep them up-to-date. The challenge is to understand and manage these changes and their impact on depending systems well in order to handle the growing volume of data annotated with ontologies and the limited documentation describing the changes. Methods We present a method to detect and characterize the changes occurring between different versions of an ontology together with an ontology of changes entitled DynDiffOnto, designed according to Semantic Web best practices and FAIR principles. We further describe the implementation of the method and the evaluation of the tool with different ontologies from the biomedical domain (i.e. ICD9-CM, MeSH, NCIt, SNOMEDCT, GO, IOBC and CIDO), showing its performance in terms of time execution and capacity to classify ontological changes, compared with other state-of-the-art approaches. Results The experiments show a top-level performance of DynDiff for large ontologies and a good performance for smaller ones, with respect to execution time and capability to identify complex changes. In this paper, we further highlight the impact of ontology matchers on the diff computation and the possibility to parameterize the matcher in DynDiff, enabling the possibility of benefits from state-of-the-art matchers. Conclusion DynDiff is an efficient tool to compute differences between ontology versions and classify these differences according to DynDiffOnto concepts. This work also contributes to a better understanding of ontological changes through DynDiffOnto, which was designed to express the semantics of the changes between versions of an ontology and can be used to document the evolution of an ontology.https://doi.org/10.1186/s13326-023-00295-7Diff computationOntology managementOntology evolutionKnowledge graph evolution
spellingShingle Sara Diaz Benavides
Silvio D. Cardoso
Marcos Da Silveira
Cédric Pruski
Analysis and implementation of the DynDiff tool when comparing versions of ontology
Journal of Biomedical Semantics
Diff computation
Ontology management
Ontology evolution
Knowledge graph evolution
title Analysis and implementation of the DynDiff tool when comparing versions of ontology
title_full Analysis and implementation of the DynDiff tool when comparing versions of ontology
title_fullStr Analysis and implementation of the DynDiff tool when comparing versions of ontology
title_full_unstemmed Analysis and implementation of the DynDiff tool when comparing versions of ontology
title_short Analysis and implementation of the DynDiff tool when comparing versions of ontology
title_sort analysis and implementation of the dyndiff tool when comparing versions of ontology
topic Diff computation
Ontology management
Ontology evolution
Knowledge graph evolution
url https://doi.org/10.1186/s13326-023-00295-7
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