SPARKLIS on QALD-6 Statistical Questions
This work focuses on the statistical questions introduced by the QALD-6 challenge. With the growing amout of semantic data, including numerical data, the need for RDF analytics beyond semantic search becomes a key issue of the Semantic Web. We have extended SPARKLIS from semantic search to RDF analytics by covering the computation features of SPARQL (expressions, aggregations and groupings). We could therefore participate to the new task on statistical questions, and we report the achieved performance of SPARKLIS. Compared to other participants, SPARKLIS does not translate spontaneous questions by users, but instead guide users in the construction of a question. Guidance is based on the actual RDF data so as to ensure that built questions are well-formed, non-ambiguous, and inhabited with answers. We show that SPARKLIS enables superior results for both an expert user (94 % correct) and a beginner user (76 % correct).
I wish to thank QALD organizers for the datacube task, Pierre-Antoine Champin for valuable feedback and suggestions, and Eléonore Jouffe for her participation to the experiment.
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