Similarity measures for document mapping: A comparative study on the level of an individual scientist
Similarity measures for document mapping: A comparative study on the level of an individual scientist
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DOI：
10.1007/s11192-007-1961-z
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发表时间：
2007
期刊：
影响因子：
3.9
通讯作者：
Christian Sternitzke;Isumo Bergmann
中科院分区：
文献类型：
--
作者：
Christian Sternitzke;Isumo Bergmann
本文探讨了收录指数、贾卡德指数和余弦指数在科技文献相似度计算中的应用。结果表明，如果在不同的文档中搜索相同的内容，包含索引通常可以提供更准确的结果，特别是在根据引文数据计算相似度时。此外，还比较了共词分析、主-动-客体结构、书目耦合、共引分析和自引链接等方法。我们发现，前两种方法往往描述的语义相似之处与基于引用的方法论所表达的知识流不同。
This paper investigates the utility of the Inclusion Index, the Jaccard Index and the Cosine Index for calculating similarities of documents, as used for mapping science and technology. It is shown that, provided that the same content is searched across various documents, the Inclusion Index generally delivers more exact results, in particular when computing the degree of similarity based on citation data. In addition, various methodologies such as co-word analysis, Subject-Action-Object (SAO) structures, bibliographic coupling, co-citation analysis, and self-citation links are compared. We find that the two former ones tend to describe rather semantic similarities that differ from knowledge flows as expressed by the citation-based methodologies.