IMPROVE-QA: An Interactive Mechanism for RDF Question/Answering Systems

Abstract

RDF Question/Answering(Q/A) systems can interpret user's question N as SPARQL query Q and return answer set $Q(D)$ over RDF repository D to the user. However, due to the complexity of linking natural phrases with specific RDF items (e.g., entities and predicates), it remains difficult to understand users' questions precisely, hence $Q(D)$ may not meet users' expectation, offering wrong answers and dismissing some correct answers. In this demo, we design an I Interactive Mechanism aiming for PRO motion V ia feedback to Q/A systems (IMPROVE-QA), a whole platform to make existing Q/A systems return more precise answers (denoted as $\mathcal Q^\prime (D)$) to users. Based on user's feedback over $Q(D)$, IMPROVE-QA automatically refines the original query Q into a new query graph $\mathcal Q^\prime $ with minimum modifications, where $\mathcal Q^\prime (D)$ provides more precise answers. We will also demonstrate how IMPROVE-QA can apply the "lesson'' learned from the user in each query to improve the precision of Q/A systems on subsequent natural language questions.