  Keyword search against structured databases has become a popular topic of
investigation, since many users find structured queries too hard to express,
and enjoy the freedom of a ``Google-like'' query box into which search terms
can be entered. Attempts to address this problem face a fundamental dilemma.
Database querying is based on the logic of predicate evaluation, with a
precisely defined answer set for a given query. On the other hand, in an
information retrieval approach, ranked query results have long been accepted as
far superior to results based on boolean query evaluation. As a consequence,
when keyword queries are attempted against databases, relatively ad-hoc ranking
mechanisms are invented (if ranking is used at all), and there is little
leverage from the large body of IR literature regarding how to rank query
results.
  Our proposal is to create a clear separation between ranking and database
querying. This divides the problem into two parts, and allows us to address
these separately. The first task is to represent the database, conceptually, as
a collection of independent ``queried units'', or ``qunits'', each of which
represents the desired result for some query against the database. The second
task is to evaluate keyword queries against a collection of qunits, which can
be treated as independent documents for query purposes, thereby permitting the
use of standard IR techniques. We provide insights that encourage the use of
this query paradigm, and discuss preliminary investigations into the efficacy
of a qunits-based framework based on a prototype implementation.
