  Existing knowledge-based question answering systems often rely on small
annotated training data. While shallow methods like relation extraction are
robust to data scarcity, they are less expressive than the deep meaning
representation methods like semantic parsing, thereby failing at answering
questions involving multiple constraints. Here we alleviate this problem by
empowering a relation extraction method with additional evidence from
Wikipedia. We first present a neural network based relation extractor to
retrieve the candidate answers from Freebase, and then infer over Wikipedia to
validate these answers. Experiments on the WebQuestions question answering
dataset show that our method achieves an F_1 of 53.3%, a substantial
improvement over the state-of-the-art.
