Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from...

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Autores principales:	Roua Jabla, Maha Khemaja, Félix Buendia, Sami Faiz
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ontology OWL ontology learning semantic analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/f0e04af5a1034af48c688f0a23ce5ad5
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id	oai:doaj.org-article:f0e04af5a1034af48c688f0a23ce5ad5
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spelling	oai:doaj.org-article:f0e04af5a1034af48c688f0a23ce5ad52021-11-25T16:37:41ZAutomatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments10.3390/app1122107702076-3417https://doaj.org/article/f0e04af5a1034af48c688f0a23ce5ad52021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10770https://doaj.org/toc/2076-3417Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.Roua JablaMaha KhemajaFélix BuendiaSami FaizMDPI AGarticleontologyOWLontology learningsemantic analysisTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10770, p 10770 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ontology OWL ontology learning semantic analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	ontology OWL ontology learning semantic analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Roua Jabla Maha Khemaja Félix Buendia Sami Faiz Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
description	Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.
format	article
author	Roua Jabla Maha Khemaja Félix Buendia Sami Faiz
author_facet	Roua Jabla Maha Khemaja Félix Buendia Sami Faiz
author_sort	Roua Jabla
title	Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
title_short	Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
title_full	Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
title_fullStr	Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
title_full_unstemmed	Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
title_sort	automatic ontology-based model evolution for learning changes in dynamic environments
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f0e04af5a1034af48c688f0a23ce5ad5
work_keys_str_mv	AT rouajabla automaticontologybasedmodelevolutionforlearningchangesindynamicenvironments AT mahakhemaja automaticontologybasedmodelevolutionforlearningchangesindynamicenvironments AT felixbuendia automaticontologybasedmodelevolutionforlearningchangesindynamicenvironments AT samifaiz automaticontologybasedmodelevolutionforlearningchangesindynamicenvironments
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Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

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