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Distributed storage and visual query processing method oriented to large-scale financial knowledge graph

A distributed storage and knowledge graph technology, applied in the field of distributed storage and visual query processing method design, can solve problems such as the influence of knowledge query efficiency in specific fields, the inability to meet the characteristics of graphs well, and achieve the effect of improving query speed.

Pending Publication Date: 2021-07-23
LIAONING UNIVERSITY
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, since knowledge graphs in different fields have their own characteristics, if a unified distributed storage mode is adopted, the characteristics of the graph cannot be well satisfied, which will affect the query efficiency of knowledge in specific domains

Method used

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  • Distributed storage and visual query processing method oriented to large-scale financial knowledge graph
  • Distributed storage and visual query processing method oriented to large-scale financial knowledge graph
  • Distributed storage and visual query processing method oriented to large-scale financial knowledge graph

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Embodiment 1

[0087] In order to test the performance of the present invention's distributed storage and visual query processing method for large-scale financial knowledge graphs, as an example of the present invention, we constructed a small cluster consisting of 5 interconnected computers, wherein the hardware configuration of the machine is Inter The computer with Core i7-8750 CPU@1.8Hz 2.00GHz processor and 8G memory is used as the host machine of the Neo4j graph database, and the other 4 servers with 64GB memory, 512GB hard disk and Ubuntu operating system are used as the equipment for deploying the HBase distributed database.

[0088]In order to test the performance of the method of the present invention, we crawled information such as basic corporate information, shareholder information, executive information, corporate news, and corporate credit required to build a financial knowledge map, and performed statistical analysis and corresponding processing on the crawled raw data. Build ...

example

[0090] (1) Use the graph segmentation method based on node density and modularity to segment the graph

[0091] For large-scale knowledge graphs, for 4 storage servers, first randomly select 4 nodes with the largest degree and no direct relationship as hot nodes, calculate the modularity of the remaining nodes joining a certain partition, and add each node to make the modularity change. In a large server, the tightness of the internal connections of the sub-graphs is ensured. by Figure 4 As an example, suppose a node i in the graph is assigned to m 1 , according to the formula of modularity, the modularity before moving is

[0092]

[0093] Where ∑cin represents the sum of edge weights in community c, and ∑tot represents the sum of edge weights connected to nodes in community c. Assign node i to m 1 The subsequent modularity is Q 2 ,E i Indicates that node i joins m 1 The number of newly added edges, k i Indicates the degree of node i.

[0094]

[0095] The cha...

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Abstract

A distributed storage and visual query processing method oriented to a large-scale financial knowledge graph comprises the following steps: firstly, constructing a graph segmentation model, carrying out initial division on the graph by utilizing node density according to financial data characteristics, and carrying out dynamic adjustment on local nodes in combination with modularity and load balancing detection; and secondly, constructing a single-table multi-column cluster distributed storage model based on HBase, and for each type of entities, storing relationships and attribute values among entities by using a single table, so that distributed storage of large-scale knowledge graph data is realized, and the problems that a single-machine storage machine is high in cost and data cannot be accessed due to a single-point fault are solved. And finally, establishing a visualized query mechanism based on Neo4i, extracting related entities and relationships in the HBase according to query semantics, and importing the extracted entities and relationships into Neo4j to form sub-maps in which users are interested for visualized display, so that complicated, difficult-to-understand and diverse knowledge in the financial field is visually presented in a graphical manner, implicit knowledge is dominated, and extrinsic knowledge is specific.

Description

technical field [0001] The invention belongs to the field of databases, and in particular relates to the design of a storage and visual query processing method for knowledge graphs, and in particular to the design of a distributed storage and visual query processing method for large-scale financial knowledge graphs. Background technique [0002] With the rapid development of information technology and Internet technology, the scale of data is continuously increasing, and the requirements for data application are also getting higher and higher. As a typical application of domain-specific knowledge data, knowledge graph uses a directed graph structure to describe entities, events or concepts that exist in the real world and the relationship between them. Among them, the nodes in the graph represent entities, events or concepts, and the edges in the graph represent the relationship between adjacent nodes. The visual display and data analysis on top of it can further dig out th...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/338G06F16/27G06F16/28G06F9/50
CPCG06F16/367G06F16/338G06F16/27G06F16/284G06F9/5083
Inventor 单晓欢宋宝燕李海海李冬
Owner LIAONING UNIVERSITY
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