{"title":"生物医学数据词典的机器阅读","authors":"N. Ashish, Arihant Patawari","doi":"10.1145/3177874","DOIUrl":null,"url":null,"abstract":"This article describes an approach for the automated reading of biomedical data dictionaries. Automated reading is the process of extracting element details for each of the data elements from a data dictionary in a document format (such as PDF) to a completely structured representation. A structured representation is essential if the data dictionary metadata are to be used in applications such as data integration and also in evaluating the quality of the associated data. We present an approach and implemented solution for the problem, considering different formats of data dictionaries. We have a particular focus on the most challenging format with a machine-learning classification solution to the problem using conditional random field classifiers. We present an evaluation using several actual data dictionaries, demonstrating the effectiveness of our approach.","PeriodicalId":15582,"journal":{"name":"Journal of Data and Information Quality (JDIQ)","volume":"180 1","pages":"1 - 20"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Machine Reading of Biomedical Data Dictionaries\",\"authors\":\"N. Ashish, Arihant Patawari\",\"doi\":\"10.1145/3177874\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article describes an approach for the automated reading of biomedical data dictionaries. Automated reading is the process of extracting element details for each of the data elements from a data dictionary in a document format (such as PDF) to a completely structured representation. A structured representation is essential if the data dictionary metadata are to be used in applications such as data integration and also in evaluating the quality of the associated data. We present an approach and implemented solution for the problem, considering different formats of data dictionaries. We have a particular focus on the most challenging format with a machine-learning classification solution to the problem using conditional random field classifiers. We present an evaluation using several actual data dictionaries, demonstrating the effectiveness of our approach.\",\"PeriodicalId\":15582,\"journal\":{\"name\":\"Journal of Data and Information Quality (JDIQ)\",\"volume\":\"180 1\",\"pages\":\"1 - 20\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Data and Information Quality (JDIQ)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3177874\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Data and Information Quality (JDIQ)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3177874","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This article describes an approach for the automated reading of biomedical data dictionaries. Automated reading is the process of extracting element details for each of the data elements from a data dictionary in a document format (such as PDF) to a completely structured representation. A structured representation is essential if the data dictionary metadata are to be used in applications such as data integration and also in evaluating the quality of the associated data. We present an approach and implemented solution for the problem, considering different formats of data dictionaries. We have a particular focus on the most challenging format with a machine-learning classification solution to the problem using conditional random field classifiers. We present an evaluation using several actual data dictionaries, demonstrating the effectiveness of our approach.