Ontology-based searching framework for digital shapes

Knowledge related to Shape Modelling is multi-faceted because of the complexity and heterogeneity of the involved resources and because different applications 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 resources and knowledge. The field needs to be formalized in order to achieve a shared conceptualization accessible by the whole scientific community and eventually 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 effective use in the Digital Shape Workbench, a searching framework for sharing resources (shapes, tools and publications) and their related knowledge.