  In recent years, chat-bot has become a new type of intelligent terminal to
guide users to consume services. However, it is criticized most that the
services it provides are not what users expect or most expect. This defect
mostly dues to two problems, one is that the incompleteness and uncertainty of
user's requirement expression caused by the information asymmetry, the other is
that the diversity of service resources leads to the difficulty of service
selection. Conversational bot is a typical mesh device, so the guided
multi-rounds Q$\&$A is the most effective way to elicit user requirements.
Obviously, complex Q$\&$A with too many rounds is boring and always leads to
bad user experience. Therefore, we aim to obtain user requirements as
accurately as possible in as few rounds as possible. To achieve this, a user
intention recognition method based on Knowledge Graph (KG) was developed for
fuzzy requirement inference, and a requirement elicitation method based on
Granular Computing was proposed for dialog policy generation. Experimental
results show that these two methods can effectively reduce the number of
conversation rounds, and can quickly and accurately identify the user
intention.
