Abstract
This paper presents the Norwegian State of Estate (SoE) dataset containing data about real estates owned by the central government in Norway. The dataset is produced by integrating cross-domain government datasets including data from sources such as the Norwegian business entity register, cadastral system, building accessibility register and the previous SoE report. The dataset is made available as Linked Data. The Linked Data generation process includes data acquisition, cleaning, transformation, annotation, publishing, augmentation and interlinking the annotated data as well as quality assessment of the interlinked datasets. The dataset is published under the Norwegian License for Open Government Data (NLOD) and serves as a reference point for applications using data on central government real estates, such as generation of the SoE report, searching properties suitable for asylum reception centres, risk assessment for state-owned buildings or a public building application for visitors.
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Notes
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An example of such a report from the UK government can be found at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/200448/SOFTE2012_final.pdf.
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Real estates can also be called real properties, properties or cadastral parcels if the properties are registered at the national cadastral system.
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Available at http://vocabs.datagraft.net/ and the names start with proDataMarket.
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The scripts are currently best visible using Chrome.
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Available at https://datahub.io/dataset/norwegiansoe.
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http://global.census.okfn.org/dataset/land accessed 20 March 2017.
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Acknowledgements
The work has been funded by the European Commission under the proDataMarket project (H2020-ICT-2014-1 644497). The authors would like to thank Bjørg Pettersen and the proDataMarket consortium for additional contributions.
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Shi, L., Sukhobok, D., Nikolov, N., Roman, D. (2017). Norwegian State of Estate Report as Linked Open Data. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10574. Springer, Cham. https://doi.org/10.1007/978-3-319-69459-7_30
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