A Study of Applying Knowledge Modelling to Evidence-Based Guidelines

This paper reports on a case-study of applying the general purpose and widely accepted methodology CommonKADS to a clinical practice guideline. CommonKADS is focussed on obtaining a compact knowledge model. However, guidelines usually contain incomplete and ambiguous knowledge. So, the resulting knowledge model will be incomplete and we will need to detect what parts of the guideline knowledge are missing. A complementary alternative, which we propose in this work, is to reconstruct the process of knowledge model construction, proposed by CommonKADS, in order to force the knowledge engineer to keep the transformation paths during knowledge modelling. That is to say, we propose to establish explicit mappings between the original medical texts and the knowledge model, storing these correspondences in a structured way. This alternative will reduce the existing gap between natural language representation and the corresponding knowledge model.