技术视角:构建实体匹配管理系统

W. Tan
{"title":"技术视角:构建实体匹配管理系统","authors":"W. Tan","doi":"10.1145/3277006.3277014","DOIUrl":null,"url":null,"abstract":"Entity matching, also known as entity resolution or reference reconciliation, is to identify when two (different) representations refer to the same real-world entity. Overcoming the entity matching problem is often a key step in today’s data preparation and integration pipeline before useful data can be produced for analysis. For example, to understand how many potential new customers there may be, a company may wish to integrate an internal repository of customer profiles to an externally sourced dataset that contains profiles of users (e.g., Twitter data). A successful entity matching process would need to discern when two heterogeneous customer profiles may actually refer to the same customer and also for the opposite, when two seemingly identical customer profiles may actually not be the same customer. For example, it is not obvious whether or not the these two records:","PeriodicalId":21740,"journal":{"name":"SIGMOD Rec.","volume":"25 1","pages":"32"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"179","resultStr":"{\"title\":\"Technical Perspective:: Toward Building Entity Matching Management Systems\",\"authors\":\"W. Tan\",\"doi\":\"10.1145/3277006.3277014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Entity matching, also known as entity resolution or reference reconciliation, is to identify when two (different) representations refer to the same real-world entity. Overcoming the entity matching problem is often a key step in today’s data preparation and integration pipeline before useful data can be produced for analysis. For example, to understand how many potential new customers there may be, a company may wish to integrate an internal repository of customer profiles to an externally sourced dataset that contains profiles of users (e.g., Twitter data). A successful entity matching process would need to discern when two heterogeneous customer profiles may actually refer to the same customer and also for the opposite, when two seemingly identical customer profiles may actually not be the same customer. For example, it is not obvious whether or not the these two records:\",\"PeriodicalId\":21740,\"journal\":{\"name\":\"SIGMOD Rec.\",\"volume\":\"25 1\",\"pages\":\"32\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"179\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIGMOD Rec.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3277006.3277014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIGMOD Rec.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3277006.3277014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 179

摘要

实体匹配,也称为实体解析或引用协调,用于识别两个(不同)表示何时引用相同的现实世界实体。在生成用于分析的有用数据之前,克服实体匹配问题通常是当今数据准备和集成管道中的关键步骤。例如,为了了解可能有多少潜在的新客户,公司可能希望将客户配置文件的内部存储库集成到包含用户配置文件的外部来源数据集(例如,Twitter数据)。一个成功的实体匹配过程需要辨别两个异类客户配置文件何时可能实际上指向同一个客户,以及相反的情况,即两个看似相同的客户配置文件何时可能实际上不是同一个客户。例如,这两项记录是否存在并不明显:
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technical Perspective:: Toward Building Entity Matching Management Systems
Entity matching, also known as entity resolution or reference reconciliation, is to identify when two (different) representations refer to the same real-world entity. Overcoming the entity matching problem is often a key step in today’s data preparation and integration pipeline before useful data can be produced for analysis. For example, to understand how many potential new customers there may be, a company may wish to integrate an internal repository of customer profiles to an externally sourced dataset that contains profiles of users (e.g., Twitter data). A successful entity matching process would need to discern when two heterogeneous customer profiles may actually refer to the same customer and also for the opposite, when two seemingly identical customer profiles may actually not be the same customer. For example, it is not obvious whether or not the these two records:
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信