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Advances in Pattern-Based Ontology Engineering
Linköping University, Sweden.
University of Maine, USA.
Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).ORCID iD: 0000-0001-8767-4136
Kansas State University, USA.
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2021 (English)Collection (editor) (Refereed)
Abstract [en]

Ontologies are the corner stone of data modeling and knowledge representation, and engineering an ontology is a complex task in which domain knowledge, ontological accuracy and computational properties need to be carefully balanced. As with any engineering task, the identification and documentation of common patterns is important, and Ontology Design Patterns (ODPs) provide ontology designers with a strong connection to requirements and a better communication of their semantic content and intent.

This book, Advances in Pattern-Based Ontology Engineering, contains 23 extended versions of selected papers presented at the annual Workshop on Ontology Design and Patterns (WOP) between 2017 and 2020. This yearly event, which attracts a large number of researchers and professionals in the field of ontology engineering and ontology design patterns, covers issues related to quality aspects of ontology engineering and ODPs for data and knowledge representation, and is usually co-located with the International Semantic Web Conference (ISWC), apart from WOP 2020, which was held virtually due to the COVID-19 pandemic. Topics covered by the papers collected here focus on recent advances in ontology design and patterns, and range from a method to instantiate content patterns, through a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations and applications.

The book provides an overview of important advances in ontology engineering and ontology design patterns, and will be of interest to all those working in the field.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2021. , p. 395
Series
Studies on the Semantic Web, ISSN 1868-1158, E-ISSN 2215-0870 ; 51
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-52468Scopus ID: 2-s2.0-85126155659ISBN: 978-1-64368-174-0 (print)ISBN: 978-1-64368-175-7 (electronic)OAI: oai:DiVA.org:hj-52468DiVA, id: diva2:1554666
Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2022-03-21Bibliographically approved

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Hammar, Karl

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
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Output format
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