Dynamic concept learning and knowledge graph generation method based on partial sequence three-branch structure

A technology of knowledge graph and concept learning, which is applied in the field of dynamic concept learning and knowledge graph generation based on partial order three-branch structure, which can solve problems such as time consumption, inconformity with dynamics, massive data knowledge processing requirements, knowledge graph generation, etc., to achieve The effect of efficient acquisition

Pending Publication Date: 2022-03-11
GUANGZHOU UNIVERSITY OF CHINESE MEDICINE
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the current partial order three-branch structure algorithm can only process static data, but cannot carry out conceptual cognitive learning and knowledge map generation based on dynamic data.
When new data is integrated, the current partial order three-branch structure algorithm needs to reconstruct the entire knowledge graph. When the amount of data is large, this will cause a lot of unnecessary time consumption
Therefore, the current partial order three-branch structure algorithm does not meet the knowledge processing requirements of dynamic and massive data.

Method used

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  • Dynamic concept learning and knowledge graph generation method based on partial sequence three-branch structure
  • Dynamic concept learning and knowledge graph generation method based on partial sequence three-branch structure
  • Dynamic concept learning and knowledge graph generation method based on partial sequence three-branch structure

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Embodiment

[0057] Such as figure 1 As shown, in the present invention, a dynamic concept learning and knowledge map generation method based on a partial order three-branch structure includes the following steps:

[0058] S1. For a given original form background K={U, M, I} and incremental form background K I ={U I ,M,I I} According to the binary relationship between the newly added object Δu and the original object u∈U, the category of the newly added object Δu is judged; the category of the object Δu specifically includes:

[0059] Based on the cognitive operator f, f(Δu) represents the attributes of the newly added object Δu, and Δu is divided into five categories:

[0060] The largest shared object, if f(Δu)=M, then Δu is the largest shared object;

[0061] mutually exclusive objects, for u∈U, if and and Then Δu and u are mutually exclusive objects

[0062] Nearest companion object, for u∈U, if Then Δu is a companion object of u; if Δu is a companion object of u, and u ...

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Abstract

The invention discloses a dynamic concept learning and knowledge graph generation method based on a partial order three-branch structure, and the method comprises the following steps: S1, for a given original-form background and an incremental-form background, judging the type of a newly-added object according to the binary relation between the newly-added object and an original object; s2, performing incremental updating on the original concept tree based on the properties of each category according to the category of the newly added object so as to realize dynamic concept cognitive learning; and S3, according to the dynamic change of the concepts in the step S2, performing incremental deletion on each concept node, content and mutual associated edges in the original knowledge graph so as to realize incremental update of the knowledge graph. According to the method, an existing partial sequence three-branch structure generation algorithm is combined, incremental learning operation is introduced, dynamic concept cognitive learning can be achieved on the basis of dynamic data, and incremental generation of the knowledge graph is achieved.

Description

technical field [0001] The invention belongs to the technical field of knowledge graphs, and in particular relates to a dynamic concept learning and knowledge graph generation method based on a partially ordered three-branch structure. Background technique [0002] With the vigorous development of the information technology industry, the information and data available to humans is showing an explosive growth trend. How to effectively process the acquired dynamic and massive data according to the thinking mode of the human brain, and extract valuable knowledge from it has become a key problem to be solved urgently. [0003] Concept cognitive learning is a method that can effectively process massive data and extract valuable knowledge from it. The knowledge map is a means to visualize the knowledge acquired through learning, so as to facilitate operations such as knowledge mining, knowledge dissemination, knowledge retrieval, and knowledge recommendation. [0004] Partial or...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06N20/00
CPCG06F16/367G06N20/00
Inventor 闫恩亮唐纯志陆丽明洪文学
Owner GUANGZHOU UNIVERSITY OF CHINESE MEDICINE
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