基于本体图的生物医学信息检索查询扩展

Liang Dong, P. Srimani, J. Wang
{"title":"基于本体图的生物医学信息检索查询扩展","authors":"Liang Dong, P. Srimani, J. Wang","doi":"10.1109/BIBM.2011.15","DOIUrl":null,"url":null,"abstract":"Query expansion based biomedical information retrieval has been studied for over two decades, most of the studies focus only on taking advantage of one vocabulary: MeSH. We propose a completely different approach utilizing an arbitrary number of controlled vocabularies from Metathesaurus. Experiment shows that our ontology based query expansion scheme achieves 8.2% and 17.7% improvement compared with schemes using pseudo relevance feedback query expansion and using no query expansion respectively. The average improvement is 24.8% in comparison to all other existing strategies. Furthermore, we identify that generalized biomedical concepts are the reason for performance degradation.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"11 1","pages":"488-493"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Ontology Graph Based Query Expansion for Biomedical Information Retrieval\",\"authors\":\"Liang Dong, P. Srimani, J. Wang\",\"doi\":\"10.1109/BIBM.2011.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Query expansion based biomedical information retrieval has been studied for over two decades, most of the studies focus only on taking advantage of one vocabulary: MeSH. We propose a completely different approach utilizing an arbitrary number of controlled vocabularies from Metathesaurus. Experiment shows that our ontology based query expansion scheme achieves 8.2% and 17.7% improvement compared with schemes using pseudo relevance feedback query expansion and using no query expansion respectively. The average improvement is 24.8% in comparison to all other existing strategies. Furthermore, we identify that generalized biomedical concepts are the reason for performance degradation.\",\"PeriodicalId\":6345,\"journal\":{\"name\":\"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)\",\"volume\":\"11 1\",\"pages\":\"488-493\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBM.2011.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM.2011.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20

摘要

基于查询扩展的生物医学信息检索已经研究了二十多年,但大多数研究都集中在利用一个词汇:MeSH。我们提出了一种完全不同的方法,利用来自metthesaurus的任意数量的受控词汇。实验表明,基于本体的查询扩展方案与使用伪相关反馈的查询扩展方案和不使用查询扩展方案相比,分别提高了8.2%和17.7%。与所有其他现有策略相比,平均改进率为24.8%。此外,我们发现广义生物医学概念是性能下降的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Ontology Graph Based Query Expansion for Biomedical Information Retrieval
Query expansion based biomedical information retrieval has been studied for over two decades, most of the studies focus only on taking advantage of one vocabulary: MeSH. We propose a completely different approach utilizing an arbitrary number of controlled vocabularies from Metathesaurus. Experiment shows that our ontology based query expansion scheme achieves 8.2% and 17.7% improvement compared with schemes using pseudo relevance feedback query expansion and using no query expansion respectively. The average improvement is 24.8% in comparison to all other existing strategies. Furthermore, we identify that generalized biomedical concepts are the reason for performance degradation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Evolution of protein architectures inferred from phylogenomic analysis of CATH Hierarchical modeling of alternative exon usage associations with survival 3D point cloud sensors for low-cost medical in-situ visualization Bayesian Classifiers for Chemical Toxicity Prediction Normal mode analysis of protein structure dynamics based on residue contact energy
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1