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Rapid learning method for large-scale knowledge base

A learning method and knowledge base technology, applied in relational databases, database models, structured data retrieval, etc., can solve problems such as difficulties in rapid learning of knowledge bases

Active Publication Date: 2019-09-24
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0012] The present invention provides a fast learning method for large-scale knowledge bases in order to overcome the defect that the rapid learning of knowledge bases described in the above prior art is relatively difficult

Method used

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  • Rapid learning method for large-scale knowledge base

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Embodiment Construction

[0073] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0074] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0075] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0076] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0077] This embodiment provides a fast learning method for a large-scale knowledge base, such as figure 1 As shown, the method includes the following steps:

[0078] S1: Knowledge extraction: extract knowledge data from heterogeneous data, knowledge data includes: entities and relationships;

[0079] S2: Knowledge representation: Construct the TransH model, and use the TransH model to repres...

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Abstract

The invention relates to a rapid learning method for a large-scale knowledge base. The rapid learning method comprises the following steps: S1, carrying out the knowledge extraction: carrying out the extraction of knowledge data from heterogeneous data, and enabling the knowledge data to be a fact triple which comprises an entity and a relationship; S2, knowledge representation: constructing a TransH model, and embedding all entities and relationships in the knowledge data into a low-dimensional vector space by utilizing the TransH model so as to realize knowledge representation of the knowledge data; S3, knowledge fusion: fusing the knowledge data represented by the knowledge in the step S2 through an RLCD model, and storing the fused knowledge data into a knowledge map; and S4, knowledge reasoning: extracting the knowledge data fused in the step S3 from the knowledge map for reasoning, and storing the knowledge data after reasoning into the knowledge map; and obtaining updated knowledge and conclusions. According to the invention, rapid learning of the knowledge base can be realized.

Description

technical field [0001] The invention relates to the field of artificial intelligence, more specifically, to a fast learning method for large-scale knowledge bases. Background technique [0002] How to obtain large-scale structured knowledge from unstructured or semi-structured Internet information and organically integrate and build a knowledge base is of great significance to the promotion and application of knowledge bases; in the process of building knowledge bases, knowledge representation Learning is the basis of knowledge acquisition and application, therefore, knowledge representation learning plays an important role in knowledge base construction. [0003] At present, the knowledge map mainly uses the triple form of (entity1, relation, entity2) to represent knowledge, which can better represent large-scale factual knowledge; however, the types of human knowledge are complex and diverse, when faced with a large number of When complex knowledge, such as people's shopp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/28G06F17/27
CPCG06F16/367G06F16/288G06F40/211G06F40/289G06F40/30Y02D10/00
Inventor 程良伦陈柯棠张凡龙
Owner GUANGDONG UNIV OF TECH
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