Feeney, Kevin, Brennan, Rob ORCID: 0000-0001-8236-362X and Mendel-Gleason, Gavin ORCID: 0000-0002-1572-9756 (2017) Linked data schemata: fixing unsound foundations. Semantic Web, 9 (1). pp. 53-75. ISSN 1570-0844
Abstract
This paper describes our tools and method for an evaluation of the practical and logical implications of combining common linked data vocabularies into a single local logical model for the purpose of reasoning or performing quality evaluations. These vocabularies need to be unified to form a combined model because they reference or reuse terms from other linked data vocabularies and thus the definitions of those terms must be imported. We found that strong interdependencies between vocabularies are common and that a significant number of logical and practical problems make this model unification inconsistent. In addition to identifying problems, this paper suggests a set of recommendations for linked data ontology design best practice. Finally we make some suggestions for improving OWL’s support for distributed authoring and ontology reuse.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Uncontrolled Keywords: | Linked Data; Reasoning; Data Quality |
Subjects: | UNSPECIFIED |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Initiatives and Centres > ADAPT |
Publisher: | IOS Press |
Official URL: | http://dx.doi.org/10.3233/SW-170271 |
Copyright Information: | © 2017 IOS |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | European Union’s Horizon 2020 research and innovation programme under grant agreement No 644055, ALIGNED project (www.aligned-project.eu), ADAPT Centre for Digital Content Technology, funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded by the European Regional Development Fund |
ID Code: | 22976 |
Deposited On: | 15 Feb 2019 13:04 by Thomas Murtagh . Last Modified 17 Jan 2023 12:41 |
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