Answering Controlled Natural Language Questions on RDF Knowledge Bases
The fast growth in number, size and availability of rdf knowledge bases (kb) is creating a pressing need for research advances that will let people consult them without having to learn structured query languages, such as sparql, and the internal organization of the kbs. In this demo, we present our Question Answering (QA) system that accepts questions posed in a Controlled Natural Language. The questions entered by the users are annotated on the y, and an ontology driven autocompletion system displays suggested patterns computed in real time from the partially completed sentence the person is typing. By following these patterns, users can enter only semantically correct questions which are unambiguously interpreted by the system. This approach assures high levels of usability and generality, which will be demonstrated by (i) the superior performance of our system on well known QA benchmarks, (ii) letting attendees suggest their own test questions, and (iii) accessing an assortment of rdf kbs that, besides the encyclopedic DBpedia from Wikipedia, will include others on specialized domains, such as music and biology.
EDBT