The increasing amount and variety of domain knowledge and the availability of increasingly large quantities of electronic literature requires new types of support for the development of complex knowledge models. In previous publications we proposed the application of so called Annotated Bayesian Networks (ABN), textually enriched probabilistic domain models, which help knowledge engineers and medical experts to find and organize the information necessary in model building. In this paper we describe an information retrieval language in which the formalized domain knowledge and the attached textual information can be accessed in an 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 available annotated domain model as contextual information. 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 cancer domain.
|Number of pages||6|
|Journal||Proceedings of the IEEE Symposium on Computer-Based Medical Systems|
|Publication status||Published - Jan 1 2002|
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Computer Science Applications