Streamlining Structured Data Markup and Agile Modelling Methods - The Practice of Enterprise Modeling Access content directly
Conference Papers Year : 2017

Streamlining Structured Data Markup and Agile Modelling Methods

Ana-Maria Ghiran
  • Function : Author
  • PersonId : 1030800
Cristina-Claudia Osman
  • Function : Author
  • PersonId : 1030801
Dimitris Karagiannis
  • Function : Author
  • PersonId : 992648

Abstract

Structured Data Markup allows Web developers to embed semantics in HTML pages, thus enabling clients (search engines, client apps etc.) to distil machine-readable resource descriptions from HTML code. This approach emerged from the Semantic Web paradigm as a powerful alternative to traditional Web scraping. Its enablers are dedicated HTML extensions (e.g., RDFa) and controlled vocabularies (e.g., Schema.org). Originating in a different context, Enterprise Modelling methods rely on diagrammatic means for describing and analysing an enterprise system in terms of key properties and conceptual abstractions. Hence, both the Semantic Web and Enterprise Modelling paradigms share a common interest in machine-processable semantics towards the goal of elevating semantics-awareness in information systems and decision support. Inspired by this overlapping, the paper proposes a mechanism for streamlining semantics between Structured Data Markup and enterprise modelling methods. Towards this goal, it employs the Resource Description Framework and the Agile Modelling Method Engineering Framework.
Fichier principal
Vignette du fichier
459826_1_En_22_Chapter.pdf (23.51 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01765259 , version 1 (12-04-2018)

Licence

Attribution

Identifiers

Cite

Ana-Maria Ghiran, Robert Andrei Buchmann, Cristina-Claudia Osman, Dimitris Karagiannis. Streamlining Structured Data Markup and Agile Modelling Methods. 10th IFIP Working Conference on The Practice of Enterprise Modeling (PoEM), Nov 2017, Leuven, Belgium. pp.331-340, ⟨10.1007/978-3-319-70241-4_22⟩. ⟨hal-01765259⟩
204 View
452 Download

Altmetric

Share

Gmail Facebook X LinkedIn More