A Framework for Statistical Entity Identification in R

This chapter introduces abstract algebra as a means of understanding and creating data quality metrics for entity resolution, the process in which records determined to represent the same real-world entity are successively located and merged. Entity resolution is a particular form of data mining that is foundational to a number of applications in both industry and government. Examples include commercial customer recognition systems and information sharing on “persons of interest” across federal intelligence agencies. Despite the importance of these applications, most of the data quality literature focuses on measuring the intrinsic quality of individual records than the quality of record grouping or integration. In this chapter, the authors describe current research into the creation and validation of quality metrics for entity resolution, primarily in the context of customer recognition systems. The approach is based on an algebraic view of the system as creating a partition of a set of entity records based on the indicative information for the entities in question. In this view, the relative quality of entity identification between two systems can be measured in terms of the similarity between the partitions they produce. The authors discuss the difficulty of applying statistical cluster analysis to this problem when the datasets are large and propose an alternative index suitable for these situations. They also report some preliminary experimental results, and outlines areas and approaches to further research in this area.

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A knowledge graph method for hazardous chemical management: Ontology design and entity identification

Neurocomputing ◽

10.1016/j.neucom.2020.10.095 ◽

2020 ◽

Author(s):

Xue Zheng ◽

Bing Wang ◽

Yunmeng Zhao ◽

Shuai Mao ◽

Yang Tang

Keyword(s):

Knowledge Graph ◽

Graph Method ◽

Chemical Management ◽

Hazardous Chemical ◽

Ontology Design ◽

Entity Identification

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Financial Entity Identification and Information Integration (FEIII) 2018 Challenge

Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets - DSMM'18 ◽

10.1145/3220547.3225218 ◽

2018 ◽

Cited By ~ 1

Author(s):

Louiqa Raschid ◽

Douglas Burdick ◽

John Grant ◽

Joe Langsam ◽

Jay Pujara ◽

...

Keyword(s):

Information Integration ◽

Entity Identification

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entity identification

Computer Science and Communications Dictionary ◽

10.1007/1-4020-0613-6_6282 ◽

2000 ◽

pp. 526-526

Author(s):

Martin H. Weik

Keyword(s):

Entity Identification

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Family history information extraction via deep joint learning

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0995-5 ◽

2019 ◽

Vol 19 (S10) ◽

Cited By ~ 7

Author(s):

Xue Shi ◽

Dehuan Jiang ◽

Yuanhang Huang ◽

Xiaolong Wang ◽

Qingcai Chen ◽

...

Keyword(s):

Family History ◽

Language Processing ◽

Family Members ◽

Decision Making Process ◽

Family History Information ◽

Health Records ◽

Joint Learning ◽

Clinical Text ◽

History Information ◽

Entity Identification

Abstract Background Family history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making process of disorder diagnosis and treatment. However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In order to extract FH information form clinical text, there is a need of natural language processing (NLP). In the BioCreative/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1) entity identification, identifying family members and their observations (diseases) mentioned in clinical text; (2) family history extraction, extracting side of family of family members, living status of family members, and observations of family members. For this task, we propose a system based on deep joint learning methods to extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.

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