Question Answering on RDF KBs using Controlled Natural Language and Semantic Autocompletion

Tracking #: 1678-2890

This paper is currently under review
Authors: 
Giuseppe Mazzeo
Carlo Zaniolo

Responsible editor: 
Guest Editors ENLI4SW 2016

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Full Paper
Abstract: 
The fast growth in number, size and availability of RDF knowledge bases (KBs) 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 paper, we present CANaLI, a Question Answering (QA) system that accepts questions posed in a Controlled Natural Language. The questions entered by the user are annotated on the fly, and a KB -driven autocompletion system displays suggestions 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 novel feature enhances the interaction with and the usability of the CANaLI which also delivers a high level of accuracy and precision. In experiments conducted on well-known QA benchmarks, including questions on the encyclopedic DBpedia and on KBs from specialized domains, such as music and medicine, CANaLI typically outperforms other QA systems.
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