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dc.contributor.authorVrandecic, Denny
dc.contributor.authorLange, Christoph
dc.contributor.authorHausenblas, Michael
dc.contributor.authorBao, Jie
dc.contributor.authorDing, Li
dc.date.accessioned2022-02-18T02:38:34Z
dc.date.available2022-02-18T02:38:34Z
dc.date.issued2010-04-27
dc.identifier.other229
dc.identifier.urihttp://archive.tw.rpi.edu/media/latest/websci10_submission_118.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.13015/4631
dc.description.abstractA number of governments have started to publish their sta- tistical data online. Some of them are adhering to Linked Data principles, others are using other standards which of- ten can be transformed in order to be published as Linked Data. RDF vocabularies, such as SCOVO (Statistical Core Vocabulary) are being used for publishing the data. Many of the current translations of the statistical data are published using simple transformation approaches. The semantics of the statistical data points, such as the real-world concepts they describe, or how they have been derived, is typically not explicit in these transformations. We propose a path towards a collaborative approach for a posteriori explicit sense-making.
dc.relation.urihttps://tw.rpi.edu/project/LOGD
dc.subjectLinking Open Government Data
dc.titleSemantics of Governmental Statistics Data


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