机器人与自动化的进步

Research on Hierarchy Structure Generation Method of Ontology Knowledge Pan-concept in Agriculture

Abstract

Yang Yang, Chen Kong, Chao Liu and Shao-wen Li

In this paper, a hierarchy building method of an uncertain ontology concept based on cloud transformation is presented. First, extracting a qualitative concept from the database through cloud transformation; second, improving the original concept leaping algorithm to obtain a synthetically formalized expression of the coarser-grained uncertain concept, which makes the algorithm-output concept hierarchy more realistic; Finally, analyzing the tea science data with this method to extract a qualitative concept and build the concept hierarchy. The experimental results show that the qualitative concept can be more accurately extracted and characterized with this method, and the concept based on the cloud model can simultaneously express its randomness and fuzziness, which makes the ontology built based on these concepts to describe the concept model more accurately and helps to objectively build agricultural ontology.