Question answering (QA) is an important research issue in natural language processing, and most state-of the-art question answering systems are based on statistical models. After witnessing recent achievements in Artificial Intelligent (AI), many businesses wish to apply those techniques to an automatic QA system that is capable of providing 24-hour customer services for their clients. However, one imminent problem is the lack of labeled training data for the specific domain. To address this issue, we propose to combine a knowledge-based approach and an automatic principle generation process to build a QA system from limited resources. Experiments conducted on a Mandarin Restaurant dataset show that our system achieves an average accuracy of 44% for 10 question types. It demonstrates that our approach can provide an effective tool when creating a QA system.
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