Identified association graph self-optimization mechanism

A self-optimizing, knowledge-mapping technology, applied in unstructured text data retrieval, special data processing applications, semantic tool creation, etc.

Inactive Publication Date: 2020-01-17
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

However, in the application scenario where a limited amount of data is provided, the deep learning algorithm cannot make an unbiased estimate of the regularity of the data.

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  • Identified association graph self-optimization mechanism

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Embodiment Construction

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0019] Such as figure 1 As shown, the model diagram of the self-optimization mechanism of the labeled association graph based on deep learning abstracts the data and the relationship between the data in the knowledge graph into a graph, combined with the semi-structured characteristics of the graph data, using graph vertices as the center, The self-adaptive parallel graph computing engine based on message passing batch processing optimizes operat...

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Abstract

The invention provides an identified association graph self-optimization mechanism based on deep learning, and the mechanism combines a knowledge graph technology and a deep learning technology, carries out the identification processing of continuously collected data through a deep learning model, and then adds the data to an association graph. Data processing is carried out through a plurality ofdistributed data storage nodes and distributed computing nodes in the association graph, and a series of global iterations are carried out through three parts of local computing, communication unitsand fence synchronization on the basis of a block synchronization parallel computing model. Self-adaptive dynamic optimal allocation of computing resources is realized according to the resource utilization rate, the processing performance and the locality of the data of the system. And carrying out continuous disambiguation analysis and clustering calculation on the data added into the associationgraph to carry out continuous simplification and correction so as to realize continuous self-optimization of the association graph.

Description

technical field [0001] The present invention relates to the fields of massive data distributed storage, labeling, knowledge graphs and deep learning, and specifically relates to a self-optimization mechanism for the labeling correlation graph based on deep learning. Background technique [0002] A self-optimization mechanism for labeled association graphs based on deep learning, with knowledge graph technology as the main body, comprehensively adopts identification technology and deep learning to ensure the self-expansion and self-optimization of association graphs. The techniques closest to the present invention are: [0003] (1) Knowledge map: knowledge map is also called scientific knowledge map, which is called knowledge domain visualization or knowledge field mapping map in the library and information industry. It is a series of different graphics showing the knowledge development process and structural relationship. Technology describes knowledge resources and their c...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36
CPCG06F16/367
Inventor 赵宏伟张卫山张瑞聪
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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