关键词: 多重共现, 多特征项共现, 多源数据, 数据模型, 知识发现

Abstract: [Purpose/significance] Various entities and their associations are the basic units that constitute a variety of occurrence phenomena in scientific literature. By mining the associations between occurrence entities, occurrence analysis can detect all aspects of the laws of scientific activities from different angles for scientific research management and researchers. It will provide a new perspective on the development of science from all angles and perspectives.[Method/process] By studying the basic theory of multiple occurrence, this paper constructs a set of unique basic theoretical system of multiple occurrence data model. The theoretical system includes definition of multiple occurrence, multiple occurrence research category, multiple occurrence variable symbols, multiple occurrence matrix definitions, multiple occurrence data organization forms, etc. In addition, based on the multiple occurrence cross-graph visualization method, this paper constructs a knowledge discovery method that can be used to analyze the occurrence relationship of three or more characteristic items, including the occurrence relevance strength, cited relevance strength and occurrence burst strength method.[Result/conclusion] Through the construction of this basic theoretical system, the research scope of occurrence phenomena is expanded, which provides the basic theory support for occurrence analysis to multi-angle and multi-dimension occurrence analysis. And through empirical research, different cases of multiple occurrence applications are selected, proving that the method can be applied to the analysis of research areas, research institutions, institutional contrast, research scholars, etc., and has good analysis results. Due to the multi-dimensional analysis and the diversification of analysis methods, this method can not only achieve the analysis effects of occurrence which includes one entity or two entities, but also reveal more extensive than the common occurrence and in-depth knowledge of content.

Key words: multiple occurrence, multiple feature items occurrence, multi-source data, data model, knowledge discovery