  Influence diagrams (IDs) are well-known formalisms extending Bayesian
networks to model decision situations under uncertainty. Although they are
convenient as a decision theoretic tool, their knowledge representation ability
is limited in capturing other crucial notions such as logical consistency. We
complement IDs with the light-weight description logic (DL) EL to overcome such
limitations. We consider a setup where DL axioms hold in some contexts, yet the
actual context is uncertain. The framework benefits from the convenience of
using DL as a domain knowledge representation language and the modelling
strength of IDs to deal with decisions over contexts in the presence of
contextual uncertainty. We define related reasoning problems and study their
computational complexity.
