  We propose a domain-independent framework for searching and retrieving facts
and relationships within natural language text sources. In this framework, an
extraction task over a text collection is expressed as a query that combines
text fragments with wild cards, and the query result is a set of facts in the
form of unary, binary and general $n$-ary tuples. A significance of our
querying mechanism is that, despite being both simple and declarative, it can
be applied to a wide range of extraction tasks. A problem in querying natural
language text though is that a user-specified query may not retrieve enough
exact matches. Unlike term queries which can be relaxed by removing some of the
terms (as is done in search engines), removing terms from a wild card query
without ruining its meaning is more challenging. Also, any query expansion has
the potential to introduce false positives. In this paper, we address the
problem of query expansion, and also analyze a few ranking alternatives to
score the results and to remove false positives. We conduct experiments and
report an evaluation of the effectiveness of our querying and scoring
functions.
