Please wait a minute...
New Technology of Library and Information Service  2007, Vol. 2 Issue (1): 72-76    DOI: 10.11925/infotech.1003-3513.2007.01.18
Current Issue | Archive | Adv Search |
Development of Co-occurrence Analysis System for SKR/MetaMap Concepts Orienting to Web Text Semantic Mining
Huang Yaming1,2   Li Guosheng1,3
1(School of Information Management and Information System (Medical),China Medical University,  Shenyang 110001, China)
2(Library of Chinese Academy of Sciences, Beijing 100080, China)
3(Neusoft Medical Systems Co., Ltd., Shenyang 110179, China)
Download: PDF (568 KB)  
Export: BibTeX | EndNote (RIS)      

This paper designes a system to analyze the output of SKR/MetaMap analysis, identifying and extracting concepts. For every concept, it assignes for describing its characteristic. Then can form Matrix Co-occurrence Matrix of concepts. This system can elevate the efficiency of information analysis,literature research, text categorization and data mining analysis.

Key wordsSKR/MetaMap      Concepts      Co-occurrence analysis      Ontology      Semantic     
Received: 16 October 2006      Published: 25 January 2007


Corresponding Authors: Huang Yaming     E-mail:
About author:: Huang Yaming,Li Guosheng

Cite this article:

Huang Yaming,Li Guosheng . Development of Co-occurrence Analysis System for SKR/MetaMap Concepts Orienting to Web Text Semantic Mining. New Technology of Library and Information Service, 2007, 2(1): 72-76.

URL:     OR

1Wettler M, Rapp R.  Computation of word associations based on the co-occurrences of words in large corpora. Dec.9,2005)
3Rindflesch T C, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform,2003,36(6):462-477
4美国国立医学图书馆 Dec.1, 2005)
5邱君瑞自然语言处理与信息检索系统 情报检索,2002(3):47-48
6Alan R, Aronson, Bethesda. Effective Mapping of Biomedical Text to the UMLS Metathesaurus: The MetaMap Program. Jan20, 2006)
7潭浩强C程序设计(第二版). 北京:清华大学出版社,1999
8Stephen P.  C++ Primer Plus 第五版(中文版). 北京:人民邮电出版社,2005
9Michael J. YoungVisual C++6从入门到精通. 北京:电子工业出版社,1999

[1] Wei Tingxin,Bai Wenlei,Qu Weiguang. Sense Prediction for Chinese OOV Based on Word Embedding and Semantic Knowledge[J]. 数据分析与知识发现, 2020, 4(6): 109-117.
[2] Deng Siyi,Le Xiaoqiu. Coreference Resolution Based on Dynamic Semantic Attention[J]. 数据分析与知识发现, 2020, 4(5): 46-53.
[3] Zhu Lu,Tian Xiaomeng,Cao Sainan,Liu Yuanyuan. Subspace Cross-modal Retrieval Based on High-Order Semantic Correlation[J]. 数据分析与知识发现, 2020, 4(5): 84-91.
[4] Zhang Dongyu,Cui Zijuan,Li Yingxia,Zhang Wei,Lin Hongfei. Identifying Noun Metaphors with Transformer and BERT[J]. 数据分析与知识发现, 2020, 4(4): 100-108.
[5] Zhang Runtong,Chen Donghua,Zhao Hongmei,Zhu Xiaomin. Computer-Assisted ICD-11 Coding Method Based on Chinese Semantic Analysis[J]. 数据分析与知识发现, 2020, 4(4): 44-55.
[6] Wei Wei,Guo Chonghui,Xing Xiaoyu. Annotating Knowledge Points & Recommending Questions Based on Semantic Association Rules[J]. 数据分析与知识发现, 2020, 4(2/3): 182-191.
[7] Tian Zhonglin,Wu Xu,Xie Xiaqing,Xu Jin,Lu Yueming. Real-time Analysis Model for Short Texts with Relationship Graph of Domain Semantics[J]. 数据分析与知识发现, 2020, 4(2/3): 239-248.
[8] Shaohua Qiang,Yunlu Luo,Yupeng Li,Peng Wu. Ontology Reasoning for Financial Affairs with RBR and CBR[J]. 数据分析与知识发现, 2019, 3(8): 94-104.
[9] Peng Guan,Yuefen Wang,Zhu Fu. Analyzing Topic Semantic Evolution with LDA: Case Study of Lithium Ion Batteries[J]. 数据分析与知识发现, 2019, 3(7): 61-72.
[10] Shiqi Deng,Liang Hong. Constructing Domain Ontology for Intelligent Applications: Case Study of Anti Tele-Fraud[J]. 数据分析与知识发现, 2019, 3(7): 73-84.
[11] Zhu Fu,Yuefen Wang,Xuhui Ding. Semantic Representation of Design Process Knowledge Reuse[J]. 数据分析与知识发现, 2019, 3(6): 21-29.
[12] Junliang Yao,Xiaoqiu Le. Semantic Matching for Sci-Tech Novelty Retrieval[J]. 数据分析与知识发现, 2019, 3(6): 50-56.
[13] Guangshang Gao. A Survey of User Profiles Methods[J]. 数据分析与知识发现, 2019, 3(3): 25-35.
[14] Chongwu Bi,Guanghui Ye,Mingqian Li,Jieyan Zeng. Discovering City Profile Based on Tag Semantic Mining[J]. 数据分析与知识发现, 2019, 3(12): 41-51.
[15] Jiao Yan,Jing Ma,Kang Fang. Computing Text Semantic Similarity with Syntactic Network of Co-occurrence Distance[J]. 数据分析与知识发现, 2019, 3(12): 93-100.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938