提高美国国立癌症研究所叙词表中层次关系的质量以实现癌症登记数据的分面查询。
Enhancing the Quality of Hierarchic Relations in the National Cancer Institute Thesaurus to Enable Faceted Query of Cancer Registry Data.
作者信息
Cui Licong, Abeysinghe Rashmie, Zheng Fengbo, Tao Shiqiang, Zeng Ningzhou, Hands Isaac, Durbin Eric B, Whiteman Lori, Remennik Lyubov, Sioutos Nicholas, Zhang Guo-Qiang
机构信息
School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX.
Department of Computer Science, University of Kentucky, Lexington, KY.
出版信息
JCO Clin Cancer Inform. 2020 May;4:392-398. doi: 10.1200/CCI.19.00124.
PURPOSE
To audit and improve the completeness of the hierarchic (or is-a) relations of the National Cancer Institute (NCI) Thesaurus to support its role as a faceted system for querying cancer registry data.
METHODS
We performed quality auditing of the 19.01d version of the NCI Thesaurus. Our hybrid auditing method consisted of three main steps: computing nonlattice subgraphs, constructing lexical features for concepts in each subgraph, and performing subsumption reasoning with each subgraph to automatically suggest potentially missing is-a relations.
RESULTS
A total of 9,512 nonlattice subgraphs were obtained. Our method identified 925 potentially missing is-a relations in 441 nonlattice subgraphs; 72 of 176 reviewed samples were confirmed as valid missing is-a relations and have been incorporated in the newer versions of the NCI Thesaurus.
CONCLUSION
Autosuggested changes resulting from our auditing method can improve the structural organization of the NCI Thesaurus in supporting its new role for faceted query.
目的
审核并完善美国国立癌症研究所（NCI）叙词表中层次结构（或“isa”关系）的完整性，以支持其作为用于查询癌症登记数据的分面分类系统的作用。
方法
我们对NCI叙词表的19.01d版本进行了质量审核。我们的混合审核方法包括三个主要步骤：计算非格性子图、为每个子图中的概念构建词汇特征，以及对每个子图进行包含推理以自动建议可能缺失的“isa”关系。
结果
共获得9512个非格性子图。我们的方法在441个非格性子图中识别出925个可能缺失的“isa”关系；在176个审查样本中，有72个被确认为有效的缺失“isa”关系，并已纳入NCI叙词表的新版本中。
结论
我们的审核方法自动建议的更改可以改善NCI叙词表的结构组织，以支持其在分面查询方面的新作用。
Zotero 插件