Symbolic representation of anatomical knowledge: concept classification and development strategies.
In this paper a novel approach to anatomy knowledge representation is described. The focus of the present research has been on the development of a representational framework where the conceptual level has been implemented by using hierarchical and nonhierarchical conceptual networks. This has allowed handling the demand for multiple views of anatomy (systemic and topographical views). The terminological level of the knowledge representation has been implemented by using a compositional strategy which has avoided the explicit storage of the terms used to express composite concepts. Hierarchical relations and composite concept representations have required supervision of both the inheritance and concept reconstruction. For this purpose heuristic knowledge has been stored in terms of consistency rules in the knowledge base. As proof of the capability of this system, we show how the knowledge base has been used to provide symbolic access to spatial information consisting of a reduced set of images from the Visible Human Dataset.
Bioengineering Department, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milan, Italy. cerveri@biomed.polimi.it
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D019412:Anatomy, Cross-Sectional; D001185:Artificial Intelligence; D019295:Computational Biology; D006801:Humans; D008953:Models, Anatomic