The fully automated generation of diagnostic codes requires a knowledge-based system which is capable of interpreting noun phrases. The sense content of the words must be analysed and represented for this purpose. The codes are then generated based on this representation.In comparison with other knowledge-based systems, a system of this kind places the emphasis on the data structures and not on the calculus; coding itself is a simple matter compared to the much more difficult task of incorporating the complex information contained in the words used in natural language in a systematic data model. Initial attempts were based on the assumption that each word was linked to one conceptual meaning, whereas such a naive viewpoint certainly no longer applies today. The notation of concepts and their relations is the task at hand.Existing notation methods include predicate logic, conceptual graphs (CGs) as proposed by J. F. Sowa [2], GRAIL as used by the GALEN Project [1] and methods developed as part of the WWW consortium, e.g. RDF's (Resource Description Frameworks). For the purpose of coding, we developed a notation system using "concept particles" back in 1989 [3]. In 1996, the resulting experience led us to represent "concept molecules" (CM), with which both complex data structures and multi-branched rules can be denoted in a simple manner [4]. In this paper we shall explain the principles behind this notation and compare it with another modern concept representation system, conceptual graphs.