Fragmented knowledge intelligent aggregation method

Abstract

The invention relates to a fragmented knowledge intelligent aggregation method. The method comprises the steps that step 1, knowledge element ontologies are defined; step 2, fragmented knowledge ontology associated aggregation is defined; step 3, associated aggregation rules based on ontology implication are established; step 4, the determination of aggregation associated rules is conducted; step5, the determination of fragmented knowledge associated rules based on the knowledge element ontologies is conducted; step 6, fragmented knowledge aggregation associated discovery is conducted; step 7, fragmented knowledge aggregation is achieved. According to the method, the associated relationship between two or more knowledge element ontologies is determined through supporting and confidence level determination, and fragmented knowledge aggregation is achieved through a strong associated method; fragmented knowledge characteristics are analyzed by the method, facing the demands of online study, original solid knowledge structures are repartitioned and dynamically aggregated into a knowledge cluster with a self-organizing capability to finally complete fragmented knowledge aggregation, and learners are guided to fully utilize fragmented time to accurately acquire meaningful knowledge contents.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, in particular to an intelligent aggregation method of fragmented knowledge. Background technique [0002] In the "Internet +" era, the deep integration of information technology and education and teaching has produced a series of new online education models represented by micro-classes and MOOCs. Learning, mobile learning and other needs. With the widespread application of new media and smart media devices, more and more people tend to use smart mobile terminals such as smartphones and tablets for reading and learning. According to the "2016 White Paper on Online Education Users" released by Sina The survey shows that 45% of users use online education products in fragmented time. It can be seen that the popularity of this new fragmented learning method marks the arrival of the era of fragmentation. Fragmented learning breaks through the constraints of time and space, and at the same time...

AI Technical Summary

Problems solved by technology

[0004] However, the existing technology has the following disadvantages: On the one hand, the current online education based on MOOCs, micro-classes, quality courses, etc., the organization of course content is still traditional, structured, and systematic, and the granularity is too coarse. , too single, it is necessary to realize the refinement, multi-dimensional and personalized organization of knowledge through effective content splitting; Therefore, it is necessary to improve the computer's ability to accurately identify user needs and accurately locate relevant knowledge points, which in turn depends on realizing the computer's "perception" of human needs and "understanding" the semantics of the corresponding digital knowledge content in the process of knowledge organization

[0006] By searching, no patent publications related to the present invention have been found

Images