Advancing Multi-Context Systems by Inconsistency Management
  Multi-Context Systems are an expressive formalism to model (possibly)
non-monotonic information exchange between heterogeneous knowledge bases. Such
information exchange, however, often comes with unforseen side-effects leading
to violation of constraints, making the system inconsistent, and thus unusable.
Although there are many approaches to assess and repair a single inconsistent
knowledge base, the heterogeneous nature of Multi-Context Systems poses
problems which have not yet been addressed in a satisfying way: How to identify
and explain a inconsistency that spreads over multiple knowledge bases with
different logical formalisms (e.g., logic programs and ontologies)? What are
the causes of inconsistency if inference/information exchange is non-monotonic
(e.g., absent information as cause)? How to deal with inconsistency if access
to knowledge bases is restricted (e.g., companies exchange information, but do
not allow arbitrary modifications to their knowledge bases)? Many traditional
approaches solely aim for a consistent system, but automatic removal of
inconsistency is not always desireable. Therefore a human operator has to be
supported in finding the erroneous parts contributing to the inconsistency. In
my thesis those issues will be adressed mainly from a foundational perspective,
while our research project also provides algorithms and prototype
implementations.
