[Objective] This paper tries to identify important and implicit semantic relations among the subject indexed papers. [Methods] Based on the subject indexed biomedical papers from MEDLINE, we proposed an algorithm consisting of subjects coordinating and indexing rules, as well as optimization rules for weighted indexing results and relation occurrences. The new algorithm was then examined with experimental disease data. [Results] With the help of domain experts’ verification, the precision of the new algorithm was higher than 95%. [Limitations] The proposed method was only appropriate for papers with subject indexing. [Conclusions] The proposed algorithm can be used to identify semantic relations among English and Chinese subjects indexed biomedical papers, and help us develop algorithms in other areas.
李晓瑛,夏光辉,李丹亚. 主题标引文献的语义关系发现研究*[J]. 现代图书情报技术, 2016, 32(7-8): 87-93.
Li Xiaoying,Xia Guanghui,Li Danya. Finding Semantic Relations Among Subject Indexed Papers. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2016.07.11.
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