  We present BewQA, a system specifically designed to answer a class of
questions that we call Bew questions. Bew questions are related to
businesses/services such as restaurants, hotels, and movie theaters; for
example, "Until what time is happy hour?". These questions are challenging to
answer because the answers are found in open-domain Web, are present in short
sentences without surrounding context, and are dynamic since the webpage
information can be updated frequently. Under these conditions, existing QA
systems perform poorly. We present a practical approach, called BewQA, that can
answer Bew queries by mining a template of the business-related webpages and
using the template to guide the search. We show how we can extract the template
automatically by leveraging aggregator websites that aggregate information
about business entities in a domain (e.g., restaurants). We answer a given
question by identifying the section from the extracted template that is most
likely to contain the answer. By doing so we can extract the answers even when
the answer span does not have sufficient context. Importantly, BewQA does not
require any training. We crowdsource a new dataset of 1066 Bew questions and
ground-truth answers in the restaurant domain. Compared to state-of-the-art QA
models, BewQA has a 27 percent point improvement in F1 score. Compared to a
commercial search engine, BewQA answered correctly 29% more Bew questions.
