  This paper studies the problem of finding typical entities when the concept
is given as a query. For a short concept such as university, this is a
well-studied problem of retrieving knowledge base such as Microsoft's Probase
and Google's isA database pre-materializing entities found for the concept in
Hearst patterns of the web corpus. However, we find most real-life queries are
long concept queries (LCQs), such as top American private university, which
cannot and should not be pre-materialized. Our goal is an online construction
of entity retrieval for LCQs. We argue a naive baseline of rewriting LCQs into
an intersection of an expanded set of composing short concepts leads to highly
precise results with extremely low recall. Instead, we propose to augment the
concept list, by identifying related concepts of the query concept. However, as
such increase of recall often invites false positives and decreases precision
in return, we propose the following two techniques: First, we identify concepts
with different relatedness to generate linear orderings and pairwise ordering
constraints. Second, we rank entities trying to avoid conflicts with these
constraints, to prune out lowly ranked one (likely false positives). With these
novel techniques, our approach significantly outperforms state-of-the-arts.
