  Keyword search is the most popular querying technique on semistructured data.
Keyword queries are simple and con- venient. However, as a consequence of their
imprecision, the quality of their answers is poor and the existing algorithms
do not scale satisfactorily.
  In this paper, we introduce the novel concept of cohesive keyword queries for
tree data. Intuitively, a cohesiveness relationship on keywords indicates that
they should form a cohesive whole in a query result. Cohesive keyword queries
allow term nesting and keyword repetition. Although more expressive, they are
as simple as flat keyword queries. We provide formal semantics for cohesive
keyword queries rank- ing query results on the proximity of the keyword
instances. We design a stack based algorithm which efficiently evaluates
cohesive keyword queries. Our experiments demonstrate that our approach
outperforms in quality previous filtering semantics and our algorithm scales
smoothly on queries of even 20 keywords on large datasets.
