As part of ongoing research, I’m building a search engine with ondemand query reduction capabilities. To our knowledge, none of the current commercial search engines provides such features.
Experimental machines that do this require the use of training sets, decision graphs and decision trees. For references on this topic, read
Query Expansion and Query Reduction in Document Retrieval
A Two-Step Approach for Tree-structured XPath Query Reduction
Unfortunately, these type of search engines are not popular, in part because are not practical at the scale of the Web and because require retraining of both the search engine and users –not to mention that these type of search machines are not precisely user-friendly.
Think about this: In general, average users are lazy searchers. They are also too busy to do neither query expansion or query reduction as we do in IR, nor they are prone to consult lookup lists, thesaurus, query logs, etc to refine their searches while surfing across databases. At any given point in time of the year the mentality of non-IR searchers is: “Don’t make me think”.
Thus, building a search engine that does ondemand query reductions for the Web (and that users will use without being forced to think) is not that easy.
We would like to hear of others working on similar research as we believe we have found a promising solution, at least partially. Ours is different from the approaches given in the above two references.
IR Thoughts 
You might want to take a look at this paper by Giridhar Kumaran and James Allan:
http://maroo.cs.umass.edu/pub/web/getpdf.php?id=806
Hi, Daniel:
Thank you for stopping by. The reference to SIRE certainly is great research. They provide what they call optimal “options” (query expansions and reductions). Their previous work uses all possible combinations of a long query.
Ours avoids this.