The increasing mount and variety of domain knowledge and the v ilability of increasingly l rge quantities of electronic liter ture requires new types of support for the development of complex knowledge models.In previous publications we proposed the application of so c lled Annotated Bayesian Networks (ABN),textually enriched probabilistic domain models,which help knowledge engineers and medical experts to find and organize the information necess ry in model building.In this paper we describe n information retriev l language in which the formalized domain knowledge nd the attached textual information c n be accessed in n integrated fashion and can be used to define various retrieval schemes and relevance measures.This language,on one hand,provides maximum flexibility for knowledge engineers to exploit the v ilable annotated domain model s contextual inform tion.On the other hand,it allows the definition of complex,high-level queries,in which the contextual use of the annotated domain model can be optimized for clinical situations.We compare the performance of the standard and the proposed query language in the ovarian c ncer domain.