关于实体决议的特刊征稿

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Journal of Data and Information Quality Pub Date : 2010-07-01 DOI:10.1145/1805286.1805292
J. Talburt, S. Madnick, Yang W. Lee
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引用次数: 0

摘要

实体解析(ER)是现代信息系统数据集成中提高数据质量的关键环节。ER涵盖了广泛的基于实体的集成方法,这些方法被称为合并/清除、记录重复删除、异构连接、身份解析和客户识别。更广泛地说,ER还包括许多重要的集成前和集成后活动,例如实体引用提取和实体关系分析。基于Fellegi-Sunter模型所描述的直接记录匹配策略,新的理论框架正在发展,以描述ER过程和结果,包括其他类型的推断和断言的参考链接技术。企业早就认识到,他们的ER过程的质量直接影响到他们的信息资产的总体价值和他们生产的信息产品的质量。政府机构和部门,包括执法部门和情报界,也在越来越多地使用电子病历作为完成任务的工具。认识到人们对ER理论和实践日益增长的兴趣,以及它对组织信息质量的影响,ACM数据与信息质量杂志(JDIQ)将专门为这一领域的创新和高质量的研究论文专门出版一期。欢迎讨论实体解决方案的任何方面的论文。
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Call for Papers Special Issue on Entity Resolution
Entity resolution (ER) is a key process for improving data quality in data integration in modern information systems. ER covers a wide range of approaches to entity-based integration, known variously as merge/purge, record de-duplication, heterogeneous join, identity resolution, and customer recognition. More broadly, ER also includes a number of important preand post-integration activities, such as entity reference extraction and entity relationship analysis. Based on direct record matching strategies, such as those described by the Fellegi-Sunter Model, new theoretical frameworks are evolving to describe ER processes and outcomes that include other types of inferred and asserted reference linking techniques. Businesses have long recognized that the quality of their ER processes directly impacts the overall value of their information assets and the quality of the information products they produce. Government agencies and departments, including law enforcement and the intelligence community, are increasing their use of ER as a tool for accomplishing their missions as well. Recognizing the growing interest in ER theory and practice, and its impact on information quality in organizations, the ACM Journal of Data and Information Quality (JDIQ) will devote a special issue to innovative and high-quality research papers in this area. Papers that address any aspect of entity resolution are welcome.
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来源期刊
ACM Journal of Data and Information Quality
ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.10
自引率
4.80%
发文量
0
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