143 results about "Graph structured data" patented technology

A dynamic foldering system automatically manages the creation and deletion of run-time dynamic folders and selection of documents, items, or object graphs found in the run-time dynamic folders. The system comprises a query / predicate for a design-time folder group based on which dynamic folders are automatically managed, a variable binding mechanism, automatic identifications of dynamic folders containing a particular object, parallel navigation, and customized combination of foldering results. The dynamic folder hierarchy is defined by a hierarchy of design-time folder groups defined by a query / predicate on a document comprising structured or semi-structured data. Modifiable criteria for creating the dynamic folder hierarchy are provided by the user. Variable binding provides powerful query / predicate definitions on hierarchical data or graph-structured data. Given a document, the system automatically identifies which dynamic folders contain the document.

Arrangements and methods for providing for the efficient implementation of ranked keyword searches on graph-structured data. Since it is difficult to directly build indexes for general schemaless graphs, conventional techniques highly rely on graph traversal in running time. The previous lack of more knowledge about graphs also resulted in great difficulties in applying pruning techniques. To address these problems, there is introduced herein a new scoring function while the block is used as an intermediate access level; the result is an opportunity to create sophisticated indexes for keyword search. Also proposed herein is a cost-balanced expansion algorithm to conduct a backward search, which provides a good theoretical guarantee in terms of the search cost.

The invention discloses a graph-structured-data generation method based on deep convolutional generative adversarial networks (DCGAN), and belongs to the field of big-data technology. The method of the invention applies the deep convolutional generative adversarial networks to the generation field of graph-structured data. According to the method of the invention, firstly, actual graph-structureddata are converted into grid structure pictures with uniqueness; then learning of a generation model is carried out through the deep convolutional generative adversarial networks; and finally, specific-scale simulated graph-structured data are generated through the generation model, which is obtained by learning, according to input parameters and requirements. According to the graph-structured-data generation method based on the deep convolutional generative adversarial networks of the invention, features are extracted mainly from the actual graph-structured data to construct the model, and thus attributes which the generated simulated graph-structured data have are enabled to more accord with the actual graph-structured data.

The embodiment of the invention discloses an image region segmentation model training method and device based on artificial intelligence, and an image region segmentation method and device based on artificial intelligence. In the model training process, a sample image set comprising at least one sample image is acquired, wherein the sample image has first annotation information, and the first annotation information can be annotation information with large granularity such as an image level. For each sample image in the sample image set, graph structure data corresponding to the sample image isgenerated, and each vertex in the graph structure data comprises at least one pixel point in the sample image. Second annotation information of the vertex is determined according to the graph structure data and the first annotation information through a graph convolution network model, wherein the granularity of the second annotation information is smaller than the granularity of the first annotation information. Since the vertexes are actually superpixel points and the second annotation information is superpixel-level annotation, in the training process, due to intervention of pixel-level annotation, strong supervision can be achieved, and the accuracy of the model is improved, and then the accuracy of image segmentation is improved.