Querying RDF Data Cubes through Natural Language

In this discussion 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 (KB), 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. The correct template is chosen by using a set of regex-like patterns, based on both syntactical and semantic features of the tokens extracted from the question. Preliminary results are encouraging and suggest a number of improvements. Over the 50 questions of the QALD-6 challenge, QA3 can process 44 questions, with 0.59 precision and and 0.62 recall, remarkably improving the state of the art in natural language question answering over data cubes.