QA3: a Natural Language Approach to Statistical Question Answering

Tracking #: 1489-2701

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

Responsible editor: 
Guest Editors ENLI4SW 2016

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Full Paper
Abstract: 
In this paper we present QA3, a question answering (QA) system over RDF cubes. The system first tags chunks of text with elements of the knowledge base, and then leverages the well-defined structure of data cubes to create the SPARQL query from the tags. For each class of questions with the same structure a SPARQL template is defined, to be filled in with SPARQL fragments obtained by the interpretation of the question. The correct template is chosen by using an original set of regex-like patterns, based on both syntactical and semantic features of the tokens extracted from the question. Preliminary results obtained using a very limited set of templates are encouraging and suggest a number of improvements. QA3 can currently provide a correct answer to 27 of the 50 questions of the test set of the task 3 of QALD-6 challenge, remarkably improving the state of the art in natural language question answering over data cubes.
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