  The use of preferences in query answering, both in traditional databases and
in ontology-based data access, has recently received much attention, due to its
many real-world applications. In this paper, we tackle the problem of top-k
query answering in Datalog+/- ontologies subject to the querying user's
preferences and a collection of (subjective) reports of other users. Here, each
report consists of scores for a list of features, its author's preferences
among the features, as well as other information. Theses pieces of information
of every report are then combined, along with the querying user's preferences
and his/her trust into each report, to rank the query results. We present two
alternative such rankings, along with algorithms for top-k (atomic) query
answering under these rankings. We also show that, under suitable assumptions,
these algorithms run in polynomial time in the data complexity. We finally
present more general reports, which are associated with sets of atoms rather
than single atoms.

Artificial Intelligence;Databases