  The abundant semi-structured data on the Web, such as HTML-based tables and
lists, provide commercial search engines a rich information source for question
answering (QA). Different from plain text passages in Web documents, Web tables
and lists have inherent structures, which carry semantic correlations among
various elements in tables and lists. Many existing studies treat tables and
lists as flat documents with pieces of text and do not make good use of
semantic information hidden in structures. In this paper, we propose a novel
graph representation of Web tables and lists based on a systematic
categorization of the components in semi-structured data as well as their
relations. We also develop pre-training and reasoning techniques on the graph
model for the QA task. Extensive experiments on several real datasets collected
from a commercial engine verify the effectiveness of our approach. Our method
improves F1 score by 3.90 points over the state-of-the-art baselines.
