Improving habitability of NLI for querying ontologies with feedback

Natural Language Interfaces (NLIs) are a viable, human-readable alternative to complex, formal query languages like SPARQL, which are typically used for accessing semantically structured data (e.g. RDF and OWL repositories). However, in order to cope with natural language ambiguities, NLIs typically support a more restricted language. A major challenge when designing such restricted languages is habitability-how easily, naturally and effectively users can use the language to express themselves within the con-strains imposed by the system. In this paper, we investigate two methods for improving the habitability of a Natural Language Interface: feedback and clarification dialogues. We model feedback by showing the user how the system interprets the query, thus suggesting repair through query reformulation. Next, we investigate how clarification dialogues can be used to control the query interpretations generated by the system. To reduce the cognitive overhead, clarification dialogues are coupled with a learning mechanism. Both methods are shown to have a positive effect on the overall performance and habitability.