  When ontologies reach a certain size and complexity, faults such as
inconsistencies, unsatisfiable classes or wrong entailments are hardly
avoidable. Locating the incorrect axioms that cause these faults is a hard and
time-consuming task. Addressing this issue, several techniques for
semi-automatic fault localization in ontologies have been proposed. Often,
these approaches involve a human expert who provides answers to
system-generated questions about the intended (correct) ontology in order to
reduce the possible fault locations. To suggest as informative questions as
possible, existing methods draw on various algorithmic optimizations as well as
heuristics. However, these computations are often based on certain assumptions
about the interacting user.
  In this work, we characterize and discuss different user types and show that
existing approaches do not achieve optimal efficiency for all of them. As a
remedy, we suggest a new type of expert question which aims at fitting the
answering behavior of all analyzed experts. Moreover, we present an algorithm
to optimize this new query type which is fully compatible with the (tried and
tested) heuristics used in the field. Experiments on faulty real-world
ontologies show the potential of the new querying method for minimizing the
expert consultation time, independent of the expert type. Besides, the gained
insights can inform the design of interactive debugging tools towards better
meeting their users' needs.
