  With the rapid growth of knowledge bases (KBs) on the web, how to take full
advantage of them becomes increasingly important. Knowledge base-based question
answering (KB-QA) is one of the most promising approaches to access the
substantial knowledge. Meantime, as the neural network-based (NN-based) methods
develop, NN-based KB-QA has already achieved impressive results. However,
previous work did not put emphasis on question representation, and the question
is converted into a fixed vector regardless of its candidate answers. This
simple representation strategy is unable to express the proper information of
the question. Hence, we present a neural attention-based model to represent the
questions dynamically according to the different focuses of various candidate
answer aspects. In addition, we leverage the global knowledge inside the
underlying KB, aiming at integrating the rich KB information into the
representation of the answers. And it also alleviates the out of vocabulary
(OOV) problem, which helps the attention model to represent the question more
precisely. The experimental results on WEBQUESTIONS demonstrate the
effectiveness of the proposed approach.
