Interactive knowledge learning method based on semantic framework

A technology of knowledge learning and semantic framework, applied in semantic analysis, machine learning, computational models, etc., can solve problems such as poor knowledge correctness and timeliness, inability to expose problems, and lack of feedback closed loops, and achieve the effect of formal knowledge being effective.

Pending Publication Date: 2022-04-12
杭州北冥星眸科技有限公司
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AI Technical Summary

Problems solved by technology

[0004] In the traditional semantic network technology, the process of construction and application is a one-way process. The lack of feedback closed loop makes the correctness and timeliness of knowledge in the network poor, and the problem cannot be exposed in time, resulting in the risk of spreading wrong knowledge.

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  • Interactive knowledge learning method based on semantic framework
  • Interactive knowledge learning method based on semantic framework
  • Interactive knowledge learning method based on semantic framework

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Embodiment Construction

[0030] In order to better understand the purpose, structure and function of the present invention, a semantic framework-based interactive knowledge learning method of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0031] An interactive knowledge learning method based on the semantic framework of the present invention, in the process of interacting with users, the method actively collects and processes knowledge according to curiosity, and verifies the authenticity of knowledge according to the degree of confidence, providing users with Provide relatively credible data.

[0032] Curiosity is a psychological concept. We use it in engineering to represent a functional module similar to humans, which is responsible for driving the function of active learning of unknown knowledge and information.

[0033] The inventive method comprises the steps:

[0034] Step 1: Add a curiosity model. According to the knowledge fr...

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Abstract

The invention discloses an interactive knowledge learning method based on a semantic framework. The method comprises the following steps: step 1, adding a curiosity model; step 2, maintaining a credibility value for each channel and the user according to the ability and credibility of question answering and different knowledge fields, and accumulating confidence coefficients of different channels for the generated knowledge; 3, redesigning a semantic network storage structure, and dividing a knowledge storage space according to confidence; 4, constructing a statistical cognition module; 5, feeding back results to different channels and users according to the screening condition of the knowledge, and adjusting the values of the credibility of the corresponding fields to form a dynamic knowledge construction closed loop; and step 6, reflecting the credibility of each channel or user on the confidence coefficient of knowledge in the next knowledge construction process. According to the method, storage of knowledge with different levels of confidence degrees is divided and isolated, and the knowledge is dynamically constructed in an external interaction process, so that the obtained formal knowledge is more effective.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to an interactive knowledge learning method based on a semantic framework. Background technique [0002] Knowledge representation methods can be divided into rule mode and graph mode: the rule-based knowledge representation method can be understood by machines, but it is not easy for users to understand; while the graph-based method has human-understandable semantics, but the efficiency of knowledge storage and knowledge reasoning lower. Semantic Web is one of the most famous models in the field of knowledge representation. Semantic network is a graph-based data structure, which can conveniently express and store natural language through custom node and relationship types, and strikes a balance between human comprehensibility and the efficiency of storage and reasoning. In a semantic network, information is expressed as a set of nodes, and the nodes are connec...

Claims

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Application Information

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IPC IPC(8): G06F16/332G06F16/33G06F40/30G06N20/00
Inventor 钱小一陈浩田华健刘逸川沈佳栋
Owner 杭州北冥星眸科技有限公司
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