document: Template-based question answering over RDF data As an increasing amount of RDF data is published as Linked Data , intuitive ways of accessing this data become more and more important . Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity . Most question answering systems translate questions into triples which are matched against the RDF data to retrieve an answer , typically relying on some similarity metric . However , in many cases , triples do not represent a faithful representation of the semantic structure of the natural language question , with the result that more expressive queries can not be answered . To circumvent this problem , we present a novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of the question . This template is then instantiated using statistical entity identification and predicate detection . We show that this approach is competitive and discuss cases of questions that can be answered with our approach but not with competing approaches .
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