This talk promotes a new query paradigm, in which incomplete and
conflicting evidences are collected from a variety of data sources and
used to answer complex queries.  The system assigns a probability to
each tuple in the answer and uses it to rank the results.  The first
part of the talk contains several examples showing how inexact matches
between data values, inexact matches between schema components, lack
of sufficient evidence to answer a given query, and violations of
constraints across data sources, can all be modeled into a single,
unified probabilistic framework.  It also illustrates how the same
probabilistic framework can be used to reason about information
disclosure.  The second half of the talk describes a new theoretical
result that is a key piece in this new probabilistic framework: how to
compute the asymptotic conditional probabilities of queries.  The
connection to random graphs will also be explained.