Semantic based search engine for 3D shapes: Design and early prototype implementation
Searching for 3D resources is gradually becoming one of those intriguing research topics that have the potential to shape how users will access and interact with the internet in the years to come. Search engines like Google Image Search, or the Princeton 3D Model Search Engine, show a glimpse of the potential for 3D searching in the Web. 3D shapes, however, present highly complex and heterogeneous objects, even more so, because of the different semantics that different applications and tools use while dealing with these resources. In the AIM@SHAPE Network of Excellence, one of the main objectives is to deal with the knowledge that is implicit in shapes and to formalize the underlying semantics through the use of ontologies. These ontologies pertain to specific scientific areas related to shape modelling and promote the reusability of domain knowledge. Modelling the semantics of shape objects constitutes a concrete step in developing an effective search mechanism for 3D resources. This mechanism is part of the Digital Shape Workbench infrastructure within AIM@SHAPE and its current state of development is presented in this paper.
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Axenopoulos, A.; Daras, P.; Papadopoulos, G.; Houstis, E. (2011)This paper presents a novel approach for fast rigid docking of proteins based on geometric complementarity. After extraction of the 3D molecular surface, a set of local surface patches is generated based on the local surface ...
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Axenopoulos, A.; Daras, P.; Papadopoulos, G.; Houstis, E. (2011)In this paper, a novel approach for fast protein-protein docking based on geometric complementarity is introduced. Complementarity matching is achieved using a rotation-invariant 3D shape descriptor, the Shape Impact ...