  When working with any sort of knowledge base (KB) one has to make sure it is
as complete and also as up-to-date as possible. Both tasks are non-trivial as
they require recall-oriented efforts to determine which entities and
relationships are missing from the KB. As such they require a significant
amount of labor. Tables on the Web, on the other hand, are abundant and have
the distinct potential to assist with these tasks. In particular, we can
leverage the content in such tables to discover new entities, properties, and
relationships. Because web tables typically only contain raw textual content we
first need to determine which cells refer to which known entities---a task we
dub table-to-KB matching. This first task aims to infer table semantics by
linking table cells and heading columns to elements of a KB. Then second task
builds upon these linked entities and properties to not only identify novel
ones in the same table but also to bootstrap their type and additional
relationships. We refer to this process as novel entity discovery and, to the
best of our knowledge, it is the first endeavor on mining the unlinked cells in
web tables. Our method identifies not only out-of-KB (``novel'') information
but also novel aliases for in-KB (``known'') entities. When evaluated using
three purpose-built test collections, we find that our proposed approaches
obtain a marked improvement in terms of precision over our baselines whilst
keeping recall stable.
