Towards portable natural language interfaces based on case-based reasoning

摘要

Natural Language Interfaces allow non-technical people to access information stored in Knowledge Bases keeping them unaware of the particular structure of the model or the underlying formal query language. Early research in the field was devoted to improve the performance of a particular system for a given Knowledge Base. Since adapting the system to new domains usually entailed considerable effort, investigating how to bring Portability to NLI became a new challenge. In this article, we investigate how Case-Based Reasoning could serve to assist the expert in porting the system so as to improve its retrieval performance. Our method HITS is based on a novel grammar learning algorithm combined with language acquisition techniques that exploit structural analogies. The learner (system) is able to engage the teacher (expert) with clarification dialogues to validate conjectures (hypotheses and deductions) about the language. Our method presents the following advantages: (i) the customization is naturally defined in the case-based cycle, (ii) the types of questions the system can deal with are not delimited in advance, and (iii) the system ‘reasons’ about precedent cases to deal with unseen questions.