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Semantic modelling and publishing of traditional data collection questionnaires and answers

Abgaz, Yalemisew orcid logoORCID: 0000-0002-3887-5342, Dorn, Amelie, Piringer, Barbara orcid logoORCID: 0000-0001-9983-1362, Wandl-Vogt, Eveline orcid logoORCID: 0000-0002-0802-0255 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2018) Semantic modelling and publishing of traditional data collection questionnaires and answers. Information, 9 (12). pp. 1-24. ISSN 2078-2489

Abstract
Extensive collections of data of linguistic, historical and socio-cultural importance are stored in libraries, museums and national archives with enormous potential to support research. However, a sizable portion of the data remains underutilised because of a lack of the required knowledge to model the data semantically and convert it into a format suitable for the semantic web. Although many institutions have produced digital versions of their collection, semantic enrichment, interlinking and exploration are still missing from digitised versions. In this paper, we present a model that provides structure and semantics to a non-standard linguistic and historical data collection on the example of the Bavarian dialects in Austria at the Austrian Academy of Sciences. We followed a semantic modelling approach that utilises the knowledge of domain experts and the corresponding schema produced during the data collection process. The model is used to enrich, interlink and publish the collection semantically. The dataset includes questionnaires and answers as well as supplementary information about the circumstances of the data collection (person, location, time, etc.). The semantic uplift is demonstrated by converting a subset of the collection to a Linked Open Data (LOD) format, where domain experts evaluated the model and the resulting dataset for its support of user queries.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:ontology; E-lexicography; semantic uplift; semantic modelling; questionnaires; linked data; linguistic linked open data
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Publisher:MDPI
Official URL:http://dx.doi.org/10.3390/info9120297
Copyright Information:© 2018 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Nationalstiftung of the Austrian Academy of Sciences under the funding scheme: Digitales kulturelles Erbe, No. DH2014/22. as part of the exploreAT!, ADAPT Centre for Digital Content Technology at Dublin City University which is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is cofunded under the European Regional Development Fund.
ID Code:23294
Deposited On:13 May 2019 15:09 by Thomas Murtagh . Last Modified 18 Jan 2021 17:13
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