  When it comes to factual knowledge about a wide range of domains, Wikipedia
is often the prime source of information on the web. DBpedia and YAGO, as large
cross-domain knowledge graphs, encode a subset of that knowledge by creating an
entity for each page in Wikipedia, and connecting them through edges. It is
well known, however, that Wikipedia-based knowledge graphs are far from
complete. Especially, as Wikipedia's policies permit pages about subjects only
if they have a certain popularity, such graphs tend to lack information about
less well-known entities. Information about these entities is oftentimes
available in the encyclopedia, but not represented as an individual page. In
this paper, we present a two-phased approach for the extraction of entities
from Wikipedia's list pages, which have proven to serve as a valuable source of
information. In the first phase, we build a large taxonomy from categories and
list pages with DBpedia as a backbone. With distant supervision, we extract
training data for the identification of new entities in list pages that we use
in the second phase to train a classification model. With this approach we
extract over 700k new entities and extend DBpedia with 7.5M new type statements
and 3.8M new facts of high precision.
