[update: the links have been updated] In this post, I list a number of SPARQL queries that show the way external sources can be used to provide enriched access to the Verrijkt Koninkrijk text. The queries go with a two-page abstract entitled “Enriched Access to a Large War Historical Text using the Back of the Book Index” I submitted to the SWAIE 2012 – Semantic Web and Information Extraction workshop I will be attending.
These queries use the back-of-the-book index that has been converted to SKOS and was subsequently aligned with a number of datasources.
The queries can be entered in the interactive SPARQL interface of the Verrijkt Koninkrijk semantic server, which can be found at http://semanticweb.cs.vu.nl/verrijktkoninkrijk/flint/ . (login: sparqltester, ww: sparqltester).
Query1: GeoNames. Get all paragrahs containing references to a place in the Dutch Province “Noord Holland”:
PREFIX niod: <http://purl.org/collections/nl/niod/>
prefix dc: <http://purl.org/dc/elements/1.1/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>SELECT DISTINCT ?subj ?bc ?par
WHERE {
?subj <http://www.geonames.org/ontology#parentADM1> <http://sws.geonames.org/2749879/>.
?bc skos:closeMatch ?subj.
?bc skos:inScheme niod:BotBScheme.
?bc niod:pageRef ?pr.
?pr niod:parRef ?par.
}
limit 100
Edit 3 oct: I continued experimenting with some other SPARQL queries and used Willem van Hage and Tomi Kauppinen’s excellent SPARQl package for R to do some quick-and-dirty statistical analysis. I used a variant of the query above, but with the province as a variable. I put the results in a pie chart showing Loe de Jong’s mentions of places found in each of the twelve provinces of the Netherlands.
And if you substitute the predicate ‘parentADM1’ for ‘parentADM2’, you get the frequencies for the individual municipalities:
I will leave the historical interpretation of these charts to the reader. Note however that a major disclaimer is needed. There are numerous errors in the data, including OCR errors, and concept mapping errors. I am sure that the municipality ‘Berkelland’ is not as important as it now seems. Also, the data should be normalized by province size to give a better idea of what is going on.
The point is however that -given the linked data- these analyses are ridiculously easy to perform with SPARQL and R.
Query2: NIOD Thesaurus Beeldbank WO2. Get all combinations of BBWO2 images and paragraphs
PREFIX niod: <http://purl.org/collections/nl/niod/>
prefix dc: <http://purl.org/dc/elements/1.1/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>SELECT DISTINCT ?img ?par
WHERE {
?object dc:subject ?subj ;
dc:relation ?img .
?subj skos:inScheme niod:ConceptScheme.
?subj skos:exactMatch ?bc.
?bc skos:inScheme niod:BotBScheme.
?bc niod:pageRef ?pr.
?pr niod:parRef ?par.
}
limit 100
