Ontology-based validation of enterprise architecture principles in enterprise models

dc.contributor.authorMontecchiari, Devid
dc.date.accessioned2024-04-19T12:24:33Z
dc.date.available2024-04-19T12:24:33Z
dc.date.issued2021
dc.description.abstractEnterprises use Enterprise Architecture Principles as a guiding set of rules to provide a basis for decision making. These principles are described using natural language and are not machine-interpretable. The validation of these principles in models is a complex and time-consuming task. The goal of this research is to help humans in this review. Annotating enterprise architecture models with an enterprise ontology and representing architecture principles as rules, it is possible to automatically check architecture principles. The proposed approach is to combine both the domain knowledge and the modeling language knowledge to reason about models, allowing the automatic check of architecture principles.
dc.description.urihttps://ceur-ws.org/Vol-2991/
dc.eventBIR 2021 Workshops and Doctoral Consortium
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/43122
dc.identifier.urihttps://doi.org/10.26041/fhnw-7087
dc.language.isoen
dc.relation.ispartofJoint Proceedings of the BIR 2021 Workshops and Doctoral Consortium co-located with 20th International Conference on Perspectives in Business Informatics Research (BIR 2021)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.spatialVienna
dc.subject.ddc330 - Wirtschaft
dc.titleOntology-based validation of enterprise architecture principles in enterprise models
dc.type04B - Beitrag Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaftde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.openAccessCategoryDiamond
fhnw.pagination197-203
fhnw.publicationStatePublished
relation.isAuthorOfPublication348a0024-d064-47f1-ac4d-e64fabf53c47
relation.isAuthorOfPublication.latestForDiscovery348a0024-d064-47f1-ac4d-e64fabf53c47
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