In the era of big data, it is possible to carry out cooperative research on the research results of researchers through papers, patents and other data, so as to study the role of researchers, and produce results in the analysis of results. For the important problems found in the research and application of reality, this paper also proposes a research scholar interest mining algorithm based on load centrality (LCBIM), which can accurately solve the problem according to the researcher's research papers and patent data. Graphs of creative algorithms in various fields of the study aggregated ideas, generated topic graphs by aggregating neighborhoods, used the generated topic information to construct with similar or similar topic spaces, and utilize keywords to construct one or more topics. The regional structure of each topic can be used to closely calculate the weight of the centrality research model of the node, which can analyze the field in the complete coverage principle. The scientific research cooperation based on the load rate center proposed in this paper can effectively extract the interests of scientific research scholars from papers and corpus.
翻译:在大数据时代,有可能通过论文、专利和其他数据对研究人员的研究成果进行合作研究,以便研究研究人员的作用,并在分析结果方面产生结果。对于在研究和应用现实中发现的重要问题,本文件还提议根据负载中心法(LCBIM)研究学者的兴趣采矿算法(LCBIM),根据研究人员的研究论文和专利数据,这种算法可以准确地解决问题。研究各个领域的创造性算法图表汇集了各种想法,通过集成邻居制作了专题图,利用产生的专题信息构建类似或类似的专题空间,并利用关键词构建一个或多个专题。每个专题的区域结构都可以用来密切计算节点中心研究模型的权重,该模型可以在全面覆盖原则中分析该领域。根据本文提出的负载率中心进行的科学研究合作可以有效地从论文和材料中提取科学研究学者的兴趣。