Please wait a minute...
New Technology of Library and Information Service  2016, Vol. 32 Issue (7-8): 87-93    DOI: 10.11925/infotech.1003-3513.2016.07.11
Orginal Article Current Issue | Archive | Adv Search |
Finding Semantic Relations Among Subject Indexed Papers
Li Xiaoying(),Xia Guanghui,Li Danya
Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
Download: PDF(563 KB)   HTML ( 38
Export: BibTeX | EndNote (RIS)      

[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.

Key wordsFinding semantic relations      Indexed papers      Coordinating rules      Threshold     
Received: 09 March 2016      Published: 29 September 2016

Cite this article:

Li Xiaoying,Xia Guanghui,Li Danya. Finding Semantic Relations Among Subject Indexed Papers. New Technology of Library and Information Service, 2016, 32(7-8): 87-93.

URL:     OR

[1] U.S. National Library of Medicine. MEDLINE Fact Sheet [EB/OL]. [2016-03-01]. .
[2] 黄勋, 游宏梁, 于洋. 关系抽取技术研究综述[J]. 现代图书情报技术, 2013(11): 30-39.
[2] (Huang Xun, You Hongliang, Yu Yang.A Review of Relation Extraction[J]. New Technology of Library and Information Service, 2013(11): 30-39.)
[3] 徐健, 张智雄, 吴振新. 实体关系抽取的技术方法综述[J].现代图书情报技术, 2008(8): 18-23.
[3] (Xu Jian, Zhang Zhixiong, Wu Zhenxin.Review on Techniques of Entity Relation Extraction[J]. New Technology of Library and Information Service, 2008(8): 18-23.)
[4] Yu H, Hatzivassiloglou V, Friedman C, et al.Automatic Extraction of Gene and Protein Synonyms from MEDLINE and Journal Articles [C]. In: Proceedings of the 2002 AMIA Annual Symposium. 2002.
[5] 宋锐, 林鸿飞, 常富洋. 中文比较句识别及比较关系抽取[J]. 中文信息学报, 2009, 23(2): 102-122.
[5] (Song Rui, Lin Hongfei, Chang Fuyang.Chinese Comparative Sentences Identification and Comparative Relations Extraction[J]. Journalof Chinese Information Processing, 2009, 23(2): 102-122.)
[6] 韩红旗, 徐硕, 桂婕, 等. 基于词形规则模板的术语层次关系抽取方法[J]. 情报学报, 2013, 32(7): 708-715.
[6] (Han Hongqi, Xu Shuo, Gui Jie, et al.Term Hierarchical Relation Extraction Method Based on Morphology Rule Template[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(7): 708-715.)
[7] Reichartz F, Korte H, Paass G.Dependency Tree Kernels for Relation Extraction from Natural Language Text [A]. // Machine Learning and Knowledge Discovery in Databases[M]. Springer Berlin Heidelberg, 2009.
[8] 孙霞, 董乐红. 基于监督学习的同义关系自动抽取方法[J]. 西北大学学报: 自然科学版, 2008, 38(1): 35-39.
[8] (Sun Xia, Dong Lehong.Automatic Extraction of Synonymy Relation Using Supervised Learning[J]. Journal of Northwest University: Natural Science Edition, 2008, 38(1): 35-39.)
[9] 庞晓东. 基于监督学习的校友实体关系抽取研究[D]. 天津: 南开大学, 2012.
[9] (Pang Xiaodong.Research on the Alumni Entity Relation Extraction Using Supervised Learning[D]. Tianjin: Nankai University, 2012.)
[10] Rozenfeld B, Feldman R.High-Performance Unsupervised Relation Extraction from Large Corpora[C]. In: Proceedings of the 6th International Conference on Data Mining. 2006: 1032-1037.
[11] 马超. 基于Web信息使用改进的无监督关系抽取方法构建交通本体[J]. 计算机系统应用, 2015, 24(12): 273-276.
[11] (Ma Chao.Using Improved Unsupervised Relation Extraction Method to Construct Traffic Ontology Based on Web[J]. Computer Systems & Applications, 2015, 24(12): 273-276.)
[12] Zhang Z.Weakly-Supervised Relation Classification for Information [C]. In: Proceedings of the 13th ACM International Conference on Information & Knowledge Management. 2004.
[13] Fan M, Zhao D, Zhou Q, et al.Distant Supervision for Relation Extraction with Matrix Completion [C]. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Maryland, USA. 2014.
[14] Sabou M, Mathieu A, Motta S.CARLET: Semantic Relation Discovery by Harvesting Online Ontologies [C]. In: Proceedings of the 5th European Semantic Web Conference. 2008.
[15] 李熙, 徐德智. 基于WordNet的概念语义相似度研究[J]. 湖南科技学院学报, 2008, 29(12): 115-116.
[15] (Li Xi, Xu Dezhi.Concept Semantic Similarity Researching Based on WordNet[J]. Journal of Hunan University of Science and Engineering, 2008, 29(12): 115-116.)
[16] U.S. National Library of Medicine. MeSH Browser [EB/OL]. [2016-03-01]. .
[17] 中国医学科学院医学信息研究所. 中文医学主题词表[EB/ OL]. [2016-03-01]. .
[17] (Institute of Medical Information, Chinese Academy of Medical Sciences. Chinese Medical Subject Headings [EB/OL]. [2016-03-01].
[18] 肖晓旦. 生物医学文献主题标引[M]. 长沙: 湖南科学技术出版社, 2005: 65-68.
[18] (Xiao Xiaodan.Biomedical Literature Subject Indexing [M]. Changsha: Hunan Science & Technology Press, 2005: 65-68.)
[1] Ya’nan Zhao,Yuqing Wang. Research on Collaborative Filtering Traveling Products Recommendation Algorithm Based on IUNCF[J]. 数据分析与知识发现, 2018, 2(7): 63-71.
[2] Wu Xinglong,Liu Xinwang . A 2-tuple Linguistic Model of Information Retrieval[J]. 现代图书情报技术, 2006, 1(6): 43-46.
[3] Su Dongchu,Chen Heping,Sun Ping. A Fast Binarization Method Based on High-Low Pass Filter for Document Image
——The Using of Digital Image Process Technics in Digital Library
[J]. 现代图书情报技术, 2005, 21(3): 43-44.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938