This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.
Design/methodology/approach: A variety of methods such as the model construction, system analysis and experiments are used. The author has improved Morris’ crossmapping technique and developed a technique for directly describing, visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.
Findings: The visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers. It can reveal more and in-depth information than analyzing co-occurrence relations between two entities. Therefore, this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.
Research limitations: The technique could only be used to analyze co-occurrence relations of three or less than three entities at present.
Practical implications: This research has expanded the study scope of co-occurrence analysis. The research result has provided a theoretical support for co-occurrence analysis.
Originality/value: There has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles. This research defines multiple co-occurrence and the research scope, develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.
Author: Hongshen Pang
Organization: Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences
Source: Chinese Journal of Library and Information Science (CJLIS)