Topic knowledge map and knowledge structure constructions with genetic algorithm, information retrieval, and multi-dimension scaling method

This work presents a novel automated approach to construct topic knowledge maps with knowledge structures, followed by its application to an internationally renowned journal. Knowledge structures are diagrams showing the important components of knowledge in study. Knowledge maps identify the locations of objects and illustrate the relationship among objects. In our study, the important components derived from knowledge structures are used as objects to be spotted in a topic knowledge map. The purpose of our knowledge structures is to find out the major topics serving as subjects of article collections as well as related methods employed in the published papers. The purpose of topic knowledge maps is to transform high-dimensional objects (topic, paper, and cited frequency) into a 2-dimensional space to help understand complicated relatedness among high-dimensional objects, such as the related degree between an article and a topic. First, we adopt independent chi-square test to examine the independence of topics and apply genetic algorithm to choose topics selection with best fitness value to construct knowledge structures. Additionally, high-dimensional relationships among objects are transformed into a 2-dimensional space using the multi-dimension scaling method. The optimal transformation coordinate matrix is also determined by using a genetic algorithm to preserve the original relations among objects and construct appropriate topic knowledge maps.