  Formal query generation aims to generate correct executable queries for
question answering over knowledge bases (KBs), given entity and relation
linking results. Current approaches build universal paraphrasing or ranking
models for the whole questions, which are likely to fail in generating queries
for complex, long-tail questions. In this paper, we propose SubQG, a new query
generation approach based on frequent query substructures, which helps rank the
existing (but nonsignificant) query structures or build new query structures.
Our experiments on two benchmark datasets show that our approach significantly
outperforms the existing ones, especially for complex questions. Also, it
achieves promising performance with limited training data and noisy
entity/relation linking results.
