Abstract
Forming the foundations of large-scale knowledge
bases, probabilistic databases have been widely
studied in the literature. In particular, probabilistic
query evaluation has been investigated intensively
as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in
large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and
maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a
variety of applications that involve prediction or
diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from
conjunctive queries to ontology-mediated queries,
and provide a detailed complexity analysis