In constraint languages for RDF graphs, such as ShEx and SHACL, constraints on nodes and their properties in RDF graphs are known as "shapes". Schemas in these languages list the various shapes that certain targeted nodes must satisfy for the graph to conform to the schema. Using SHACL, we propose in this paper a novel use of shapes, by which a set of shapes is used to extract a subgraph from an RDF graph, the so-called shape fragment. Our proposed mechanism fits in the framework of Linked Data Fragments. In this paper, (i) we define our extraction mechanism formally, building on recently proposed SHACL formalizations; (ii) we establish correctness properties, which relate shape fragments to notions of provenance for database queries; (iii) we compare shape fragments with SPARQL queries; (iv) we discuss implementation options; and (v) we present initial experiments demonstrating that shape fragments are a feasible new idea.

Researchers have discovered characteristics of human 3D vision in a computer vision network only designed to "view" in 2D. The brain detects 3D shape fragments such as bumps, hollows, shafts, and spheres in the beginning stages of object vision--a newly discovered strategy of natural intelligence. The researchers found that same strategy in artificial intelligence networks trained to recognize visual objects. "…I never would have guessed in a million years that you would see the same thing happening in Alexnet, which is only trained to translate 2D photographs into object labels." Their new paper in Current Biology details how neurons in area V4, the first stage specific to the brain's object vision pathway, represent 3D shape fragments, not just the 2D shapes used to study V4 for the last 40 years. The researchers then identified nearly identical responses of artificial neurons, in an early stage (layer 3) of AlexNet, an advanced computer vision network.

You are free to share this article under the Attribution 4.0 International license. Researchers have discovered characteristics of human 3D vision in a computer vision network only designed to "view" in 2D. The brain detects 3D shape fragments such as bumps, hollows, shafts, and spheres in the beginning stages of object vision--a newly discovered strategy of natural intelligence. The researchers found that same strategy in artificial intelligence networks trained to recognize visual objects. "…I never would have guessed in a million years that you would see the same thing happening in AlexNet, which is only trained to translate 2D photographs into object labels." Their new paper in Current Biology details how neurons in area V4, the first stage specific to the brain's object vision pathway, represent 3D shape fragments, not just the 2D shapes used to study V4 for the last 40 years.

The brain detects 3-D shape fragments (bumps, hollows, shafts, spheres) in the beginning stages of object vision--a newly discovered strategy of natural intelligence that Johns Hopkins University researchers also found in artificial intelligence networks trained to recognize visual objects. A new paper in Current Biology details how neurons in area V4, the first stage specific to the brain's object vision pathway, represent 3-D shape fragments, not just the 2-D shapes used to study V4 for the last 40 years. The Johns Hopkins researchers then identified nearly identical responses of artificial neurons, in an early stage (layer 3) of AlexNet, an advanced computer vision network. In both natural and artificial vision, early detection of 3-D shape presumably aids interpretation of solid, 3-D objects in the real world. "I was surprised to see strong, clear signals for 3-D shape as early as V4," said Ed Connor, a neuroscience professor and director of the Zanvyl Krieger Mind/Brain Institute.