Application method of knowledge graph in zero-order learning

Abstract

The invention provides an application method of a knowledge graph in zero-order learning. The method comprises the following steps: firstly, training by utilizing a ResNet deep convolutional neural network model to obtain visual features of an image; then, utilizing a wordnet knowledge graph to construct a relational graph between categories; calculating the weight relationship according to the distance between the nodes; then, optimizing nodes in the relation graph by utilizing a GraphSAGE (Graphical Spatial Absorption Gradient Error) algorithm; mapping the semantic features of the optimizedclass nodes to a dimension space which is the same as the visual features by using a graph convolutional neural network; and finally, searching a category closest to the Euclidean distance of the visual features, and taking the category as a judged category. According to the method, the knowledge graph is used in the zero-order learning task, the relation graph between the categories is constructed, more priori knowledge is added, the relation between the categories is utilized, and the GraphSAGE algorithm is introduced, so that the nodes in the constructed relation graph can be optimized, andthe description of the nodes is more perfect. And the final classification result also has better performance.

Description

technical field [0001] The invention belongs to the technical field of zero-order learning, and the invention uses the knowledge map and the GraphSAGE algorithm on the zero-order learning task. Background technique [0002] In the zero-time learning, each category and its corresponding semantic features will be given. The semantic features here include the attributes of the categories, such as describing the size, color, etc. of these categories, and can also be the word vectors corresponding to these categories. However, these semantic features are only one by one, and these categories are not well connected, and the degree of "intimacy" of the connection between these categories cannot be intuitively expressed. The knowledge graph just has the ability to integrate knowledge and connect knowledge. In addition, since the GraphSAGE algorithm can iteratively learn and aggregate neighbor node information, the use of GraphSAGE can play a role in optimizing the node class in the...

AI Technical Summary

Problems solved by technology

However, these semantic features are only one by one, and these categories are not well connected, and the degree of "intimacy" between these categories cannot be expressed intuitively.

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