Hi George
Wandora's SPARQL extractor requires that you define all namespaces used in your query. Missing namespace definition results an exception in Wandora. Thus, you need to add definition
- Code: Select all
PREFIX dbpedia-owl: <http://dbpedia.org/ontology/>
in front of your query. After the addition, your query looks like
- Code: Select all
PREFIX dbpedia-owl: <http://dbpedia.org/ontology/>
SELECT DISTINCT ?band ?member
WHERE
{
?s dbpedia-owl:genre <http://dbpedia.org/resource/Hard_Rock>;
dbpedia-owl:artist ?band.
?band dbpedia-owl:bandMember ?member.
}
You'll find all predefined namespaces of DBpedia in
http://dbpedia.org/sparql?nsdecl. Now you should be able execute the query without exceptions. However, DBpedia's public SPARQL endpoint (
http://dbpedia.org/sparql) gives empty result set for the query. Thus, performing SPARQL extraction in Wandora doesn't add topics nor associations in your topic map. Perhaps DBPedia doesn't know much about hard rock

Speaking more generally about Wandora's
RDF import and
SPARQL extract features. If you have a valid RDF/XML file, you can import it to Wandora using option File -> Import -> Simple RDF XML Import... This generic RDF import feature doesn't really respect any RDF vocabularies used in the RDF file. The conversion from RDF to Topic Maps in very simple and described in
http://www.wandora.org/wiki/Importing_RDF. Usually you need to clean up the topic map a little after a generic RDF import. Addition to this simple RDF importer Wandora has several RDF extractors for specific RDF vocabularies, for example OWL, SKOSS and Dublin Core. These extractors
try to respect the specific RDF vocabulary while transforming an RDF file to topic maps. These extractors locate in menu File -> Extract -> Simple RDF formats.
And then there is the SPARQL extractor that connects directly to a SPARQL end-point and transforms the result set to topic maps. At the moment SPARQL extractor can not be used to transform ready-made SPARQL result set files nor feeds. It really requires a SPARQL end-point. As the SPARQL result set is essentially an information table, the transformation converts each result set row to an association in topic map. Remember, associations are hyper edges i.e. they may contain more than two nodes. In my personal opinion, the conversion is very clear but may require some manual actions, depending on your vision of sought information model, of course.
Kind Regards,
Aki / Wandora Team