  The web contains countless semi-structured websites, which can be a rich
source of information for populating knowledge bases. Existing methods for
extracting relations from the DOM trees of semi-structured webpages can achieve
high precision and recall only when manual annotations for each website are
available. Although there have been efforts to learn extractors from
automatically-generated labels, these methods are not sufficiently robust to
succeed in settings with complex schemas and information-rich websites.
  In this paper we present a new method for automatic extraction from
semi-structured websites based on distant supervision. We automatically
generate training labels by aligning an existing knowledge base with a web page
and leveraging the unique structural characteristics of semi-structured
websites. We then train a classifier based on the potentially noisy and
incomplete labels to predict new relation instances. Our method can compete
with annotation-based techniques in the literature in terms of extraction
quality. A large-scale experiment on over 400,000 pages from dozens of
multi-lingual long-tail websites harvested 1.25 million facts at a precision of
90%.
