  It is known that annotating named entities in unstructured and
semi-structured data sets by their concepts improves the effectiveness of
answering queries over these data sets. As every enterprise has a limited
budget of time or computational resources, it has to annotate a subset of
concepts in a given domain whose costs of annotation do not exceed the budget.
We call such a subset of concepts a {\it conceptual design} for the annotated
data set. We focus on finding a conceptual design that provides the most
effective answers to queries over the annotated data set, i.e., a {\it
cost-effective conceptual design}. Since, it is often less time-consuming and
costly to annotate general concepts than specific concepts, we use information
on superclass/subclass relationships between concepts in taxonomies to find a
cost-effective conceptual design. We quantify the amount by which a conceptual
design with concepts from a taxonomy improves the effectiveness of answering
queries over an annotated data set. If the taxonomy is a tree, we prove that
the problem is NP-hard and propose an efficient approximation and
pseudo-polynomial time algorithms for the problem. We further prove that if the
taxonomy is a directed acyclic graph, given some generally accepted hypothesis,
it is not possible to find any approximation algorithm with reasonably small
approximation ratio for the problem. Our empirical study using real-world data
sets, taxonomies, and query workloads shows that our framework effectively
quantifies the amount by which a conceptual design improves the effectiveness
of answering queries. It also indicates that our algorithms are efficient for a
design-time task with pseudo-polynomial algorithm being generally more
effective than the approximation algorithm.
