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Self-learning method based on 6W knowledge representation

A self-learning method and knowledge representation technology, applied in the direction of knowledge expression, can solve the problems of limited expansion capacity and obstacles to the construction of intelligent wisdom system

Active Publication Date: 2016-08-10
KARAMAY HONGYOU SOFTWARE
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the existing technology, the expansion capacity of standard knowledge is limited, which hinders the construction of intelligent intelligent system

Method used

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  • Self-learning method based on 6W knowledge representation
  • Self-learning method based on 6W knowledge representation
  • Self-learning method based on 6W knowledge representation

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Embodiment Construction

[0086] The present invention will be described in detail below in conjunction with the drawings:

[0087] The present invention provides a self-learning method based on 6W knowledge representation. The 6W principle originally refers to the "6W principle" of Nobel Prize winner British writer Kipling to think about and solve problems. On this basis, it is clear Business logic 6W description methods, namely "Where (where), When (what time), Who (for whom), Which (for what), What (what to do, including how to do), Why (why).

[0088] Since data is connected to information, information is connected to knowledge, and knowledge is connected to wisdom, data, information, and knowledge can be solidified by concepts. Knowledge can be understood as the connection between concepts and concepts. Concepts are uniquely identified by semantics. In the knowledge system, The concept is expressed with a logo; the connection is marked with a symbol. Identification and labeling are combined into knowl...

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Abstract

The invention provides a self-learning method based on 6W knowledge representation, comprising the following steps: defining an identification structure of each concept; defining an annotation structure for describing the link between concepts; defining a knowledge code for describing knowledge; defining knowledge credibility rules for evaluating the credibility of knowledge, and getting knowledge credibility; defining a knowledge structure organization mode, wherein the knowledge structure organization mode is composed of an identification code, an annotation code, the knowledge code and the knowledge credibility; and self-learning knowledge based on the knowledge structure organization mode. According to the self-learning method based on 6W knowledge representation provided by the invention, an identification structure, an annotation structure and a knowledge structure are defined respectively, and knowledge is expanded quickly and efficiently based on the defined identification structure, the annotation structure and the knowledge structure, which is conductive to building an intelligent wisdom system.

Description

Technical field [0001] The present invention belongs to the field of information processing technology, and specifically relates to a self-learning method based on 6W knowledge representation. Background technique [0002] The construction of an intelligent intelligent system will be the theme of the future. The foundation of an intelligent intelligent system is knowledge. The amount of knowledge possessed by an intelligent intelligent system directly affects the performance of an intelligent intelligent system. In the existing technology, the ability to expand the amount of standard knowledge is limited, which hinders the construction of intelligent intelligent systems. Summary of the invention [0003] In view of the defects in the prior art, the present invention provides a self-learning method based on 6W knowledge representation, which can effectively solve the above-mentioned problems. [0004] The technical scheme adopted by the present invention is as follows: [0005] The p...

Claims

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Application Information

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IPC IPC(8): G06N5/02
Inventor 夏冬梅
Owner KARAMAY HONGYOU SOFTWARE
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