基于文献计量共引分析的图书情报数据整合方法

Integration Method of Library and Information Data Based on Bibliometric Cocitation Analysis

【目的/意义】为了提高图书情报的实时检索能力,需要进行图书情报共引数据整合模型设计。【方法/过程】提出一种基于文献计量共引分析的图书情报数据的整合方法,构建图书情报文献计量共引数据整合的射频标签识别模型,采用RFID标签技术进行图书情报文献计量共引数据的自动采样,对采样的大数据采用语义相似度特征提取方法进行信息融合;结合文献计量共引分析方法进行图书情报数据的自适应聚类分析和整合分类,构建反映图书情报归类的语义本体模型。通过自相关特征匹配方法实现对图书情报文献计量共引数据的优化分类检索和整合。【结果/结论】测试结果表明,采用该模型进行图书情报文献计量共引数据整合的分类性能较好,数据检索的查全率和查准率较高,提高了图书情报的检索效率。

【Purpose/significance】In order to improve the real-time retrieval ability of library information, it is necessary to carry out the design of library and information co-integration data integration model.【Method/process】Propose a method of integration of library information data based on literature and measurement co-citation analysis,construct a radio frequency tag recognition model for the integration of library and information bibliometric data,use RFID tag technology to automatically sample the co-citation data of library and information literature, and use semantic similarity feature extraction method for information fusion of sampled big data.The co-citation analysis method is used for adaptive clustering analysis and integrated classification of library information data, and a semantic ontology model reflecting the classification of library information is constructed. Through the autocorrelation feature matching method, the optimized classification and retrieval of the co-citation data of the library and information literature is realized.【Result/conclusion】The test results show that the classification performance of the library and literature bibliographic data integration is better, and the recall and precision of data retrieval are higher, which improves the retrieval efficiency of library information.