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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (8): 88-97    DOI: 10.11925/infotech.2096-3467.2018.0178
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Matching Strategies for Institution Names in Literature Database
Sun Haixia1,2, Wang Lei2, Wu Yingjie2, Hua Weina1, Li Junlian2()
1School of Information Management, Nanjing University, Nanjing 210093, China
2Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China
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Abstract  

[Objective] This paper designs and implements matching strategies for institution names in literature database, aiming to regulate their storage and management. [Methods] We first established seven name matching rules based on their regions, types and naming characteristics. Then, we designed four hybrid matching strategies combining rules and Levenstein distance. Finally, we evaluated the four hybrid strategies with institution names from the papers indexed by Chinese Biomedical Literature (CBM) database during 2006-2011. [Results] More than six million affiliation strings from CBM were matched, which included higher education institutions, hospitals and research institutes. We found that the hybrid matching strategy based on region, naming characteristics and Levenstein distance obtained the highest precision (all above 80%), recall (64.82%), and F-value (71.66%). [Limitations] The rules and related dictionary were mainly constructed with human experience and their coverage is limited. There are some errors in the identifying institution names. The proposed strategy cannot address the issues caused by the transformative actions of institutions. [Conclusions] The proposed strategies could improve the performance of scientific research literature databases.
Key words： Information Retrieval      Normalization of Affiliation Strings      Similarity Measure      Hybrid Strategy      Literature Database     
Received: 11 February 2018      Published: 08 September 2018
ZTFLH:  TP393  
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Haixia Sun
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Cite this article:

Sun Haixia,Wang Lei,Wu Yingjie,Hua Weina,Li Junlian. Matching Strategies for Institution Names in Literature Database. Data Analysis and Knowledge Discovery, 2018, 2(8): 88-97.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0178     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I8/88

FP FN
LR R1
CR R2
LFR R3-R6 R7
机构分类 关键特征词示例
医院 医院、临床中心、门诊中心、门诊部、…
医学高等教育机构 学院、大学、学校、学部、…
医学科学研究机构 科学院、研究所、研究院、研究中心、创新中心、…
序号 作者机构字符串常见结构 示例
1 ‘机构’+ ‘逗号’+ ‘省份名城市名’+‘邮编’ 昆山市第一人民医院肿瘤科, 江苏昆山 215300
2 ‘机构’+ ‘逗号’+‘城市名’+‘邮编’ 上海复旦大学附属华山医院神外科, 上海 200040
3 ‘机构’+‘逗号’+ ‘省份名’+‘邮编’ 昆山市第一人民医院, 江苏省 215300
4 ‘机构’+‘逗号’+ ‘邮编’ 江苏省南通大学附属肿瘤医院, 226361
5 ‘机构’+ ‘邮编’ 江苏省南通大学附属肿瘤医院 226361
6 ‘机构’ 安徽医科大学第一附属医院消化内科
测试数据集分组 基础数据集合 新增数据集合
序号 CBM收录年份范围 机构类别 去重后机构名称串 序号 CBM收录年份范围 机构类别 去重后机构名称串
第一组(T1) TBD1 2006-2008 高等院校 22 685 TID1 2009-2011 高等院校 10 192
研究所 11 178 研究所 5 182
医院 93 895 医院 59 937
合计 127 758 合计 75 311
第二组(T2) TBD2 2006-2009 高等院校 26 943 TID 2 2010-2011 高等院校 5 932
研究所 13 195 研究所 3 165
医院 113 554 医院 40 281
合计 153 692 合计 49 378
第三组(T2) TBD3 2006-2010 高等院校 31 014 TID3 2011 高等院校 1 862
研究所 15 051 研究所 1 313
医院 133 003 医院 20 833
合计 179 068 合计 24 008
方案 T1 T2 T3
P R F值 P R F值 P R F值
C1 71.15% 62.26% 66.41% 72.68% 68.66% 70.62% 72.79% 74.37% 73.57%
C2 71.23% 60.80% 65.60% 72.23% 66.82% 69.42% 72.45% 72.92% 72.69%
C3 80.72% 53.29% 64.20% 80.56% 59.22% 68.26% 80.11% 64.82% 71.66%
C4 80.77% 51.10% 62.59% 80.46% 57.20% 66.86% 80.00% 63.17% 70.59%
方案 高等院校 科研院所 医院
T1 T2 T3 T1 T2 T3 T1 T2 T3
C1 72.55% 72.37% 68.84% 79.51% 79.64% 77.94% 71.05% 72.33% 72.85
C2 72.35% 71.86% 67.70% 77.33% 78.79% 77.08% 71.05% 72.25% 72.77%
C3 74.43% 74.24% 70.41% 84.91% 85.41% 84.83% 81.75% 81.45% 80.79%
C4 74.44% 74.00% 69.51% 77.46% 79.57% 77.27% 81.61% 81.25% 80.94%
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