Interactive knowledge learning method based on semantic framework

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...

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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