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Knowledge graph-oriented large-scale data increment processing method

A large-scale data and knowledge map technology, applied in the field of large-scale data incremental processing for knowledge maps

Pending Publication Date: 2020-07-07
军事科学院系统工程研究院系统总体研究所
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Problems solved by technology

Most of the current large-scale graphs or networks change dynamically with time, and the existing graph segmentation methods are mainly oriented to static graph data, which cannot meet the needs of reality. Therefore, solving the segmentation problem of such large-scale dynamic graphs has become the research focus of the present invention

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  • Knowledge graph-oriented large-scale data increment processing method
  • Knowledge graph-oriented large-scale data increment processing method
  • Knowledge graph-oriented large-scale data increment processing method

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Embodiment Construction

[0015] Such as figure 1 As shown, the present invention proposes a large-scale data incremental processing method for knowledge graphs for large-scale dynamic graph data. First, the existing graph segmentation algorithm is used to divide the initial graph into multiple sub-graphs; Record the change operation of the graph in the time slice cycle, and merge the change operations in the same time slice cycle to form the incremental sequence of the graph; according to the principle of load balancing of each sub-graph, the incremental sequence of the graph is mapped to a point and edge Insertion, deletion, and edge weight update operations; calculate the closeness matrix between subgraphs, if the closeness between subgraphs is greater than the closeness inside the subgraph, dynamically adjust the membership relationship between nodes and subgraphs , until the subgraphs meet the requirements of internal high cohesion and external low coupling. The specific process is as follows:

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Abstract

The invention discloses a knowledge graph-oriented large-scale data increment processing method. The method comprises the steps of (10) segmenting an initial graph into a plurality of sub-graphs by utilizing an existing graph segmentation algorithm; (20) obtaining an increment sequence of the graph in the time slice period; (30) mapping the incremental sequence of the graph into insertion and deletion operations of points and edges and weight updating operation of the edges according to a load balancing principle of each sub-graph; (40) calculating a compactness matrix among the sub-graphs; and (50) if the closeness between the sub-graphs is greater than the closeness in the sub-graphs, dynamically adjusting the subordinating relationship between the nodes and the sub-graphs until the sub-graphs meet the requirements of high internal cohesion and low external coupling. According to the method, only incremental nodes or edges are dynamically allocated to the corresponding sub-graphs, sothat the calculation overhead and the time cost are reduced; dynamic adjustment of the sub-graphs is achieved by adjusting part of the nodes, re-segmentation of the whole sub-graphs is avoided, and dynamic maintenance expenditure is reduced.

Description

technical field [0001] The invention belongs to the technical field of graph databases, in particular for large-scale dynamic graph data, and proposes a large-scale data incremental processing method oriented to knowledge graphs. Background technique [0002] Graph is a kind of abstract data structure commonly used in computer science. The universality of graph enables the actual network in the real world to be abstracted into a graph data model representation. It has broad application prospects in the field of data processing technology based on computer databases. At present, it has been widely used in fields such as computer science, linguistics, logic, physics, chemistry, and telecommunications engineering. However, with the rapid development of network and computer technology, the rapid growth of the actual network scale leads to the increasing scale of the graph; at the same time, it also intensifies the dynamic evolution of the real network, resulting in the continuou...

Claims

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Application Information

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IPC IPC(8): G06F16/901
CPCG06F16/9024
Inventor 刘颖朱连宏关礼安白新有张巍张洋铭陈剑罗承昆
Owner 军事科学院系统工程研究院系统总体研究所
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