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Open AccessArticle

Semantic Modelling and Publishing of Traditional Data Collection Questionnaires and Answers

1
ADAPT Centre, School of Computing, Dublin City University, Dublin 9, Ireland
2
Austrian Centre for Digital Humanities, Austrian Academy of Sciences, 1010 Vienna, Austria
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Author to whom correspondence should be addressed.
This paper is an extended version of our conference paper: Abgaz, Y.; Dorn, A.; Piringer, B.; Wandl-Vogt, E.; Way, A. A Semantic Model for Traditional Data Collection Questionnaires Enabling Cultural Analysis. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 7–12 May 2018; pp. 21–29.
Information 2018, 9(12), 297; https://doi.org/10.3390/info9120297
Received: 15 September 2018 / Revised: 25 October 2018 / Accepted: 21 November 2018 / Published: 24 November 2018
(This article belongs to the Special Issue Towards the Multilingual Web of Data)
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. View Full-Text
Keywords: ontology; E-lexicography; semantic uplift; semantic modelling; questionnaires; linked data; linguistic linked open data ontology; E-lexicography; semantic uplift; semantic modelling; questionnaires; linked data; linguistic linked open data
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Abgaz, Y.; Dorn, A.; Piringer, B.; Wandl-Vogt, E.; Way, A. Semantic Modelling and Publishing of Traditional Data Collection Questionnaires and Answers. Information 2018, 9, 297.

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