Statistic data is an important sub-category of open data; it is interesting for many applications, including but not limited to data journalism, as such data is typically of high quality, and reflects (under an aggregated form) important aspects of a society's life such as births, immigration, economic output etc. However, such open data is often not published as Linked Open Data (LOD) limiting its usability.We provide a conceptual model for the open data comprised in statistic files published by INSEE, the leading French economic and societal statistics institute. Then, we describe a novel method for extracting RDF LOD populating an instance of this conceptual model. Our method was used to produce RDF data out of 20k+ Excel spreadsheets, and our validation indicates a 91% rate of successful extraction.
To cite this version:Tien Duc Cao, Ioana Manolescu, Xavier Tannier. Extracting statistical mentions from textual claims to provide trusted content.Abstract. Claims on statistic (numerical) data, e.g., immigrant populations, are often fact-checked. We present a novel approach to extract from text documents, e.g., online media articles, mentions of statistic entities from a reference source. A claim states that an entity has certain value, at a certain time. This completes a fact-checking pipeline from text, to the reference data closest to the claim. We evaluated our method on the INSEE dataset and show that it is efficient and effective.
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