Knowledge related to Shape Modelling is multi-faceted because of the complexity and heterogeneity of the involved resources and because different ap- plications may cast different semantics on them. A fast evolution of the field is now conditioned by how research teams will be able to communicate and share re- sources and knowledge. The field needs to be formalized in order to achieve a shared conceptualization accessible by the whole scientific community and even- tually to ensure an actual exploitation of its knowledge within the Semantic Web. In this context, the main objective of the Network of Excellence AIM@SHAPE is twofold: on the one hand to devise tools to capture the implicit semantics of digital shapes, and on the other hand to encode and formalize the domain knowledge into context-dependent ontologies. The paper describes the first results in the direction of developing an ontology for shape acquisition and reconstruction and its effec- tive use in the Digital Shape Workbench, a searching framework for sharing re- sources (shapes, tools and publications) and their related knowledge.