Entity Matching for Digital World: A Modern Approach using Artificial Intelligence and Machine Learning

Article Crossref DOI

10.34257/GJCSTDVOL23IS1PG35

Article ID

6NP83

Exploring digital intelligence and software for advanced digital innovation.

Entity Matching for Digital World: A Modern Approach using Artificial Intelligence and Machine Learning

K. Victor Rajan
K. Victor Rajan Atlantic International University
Edward Lambert
Edward Lambert
DOI
10.34257/GJCSTDVOL23IS1PG35
High-tech microchip on digital circuit board, illustrating innovation in electronics and science research.

Abstract

Entity matching is the field of research solving the problem of identifying similar records which refer to the same real-world entity. In today’s digital world, business organizations deal with large amount of data like customers, vendors, manufacturers, etc. Entities are spread across various data sources and failure to correlate two records as one entity can lead to confusion. Relationships and patterns would be missed. Aggregations and calculations won’t make any sense. It is a significant data integration effort that often arises when data originate from different sources. In such scenarios, we understand the situation by linking records and then track entities from a person to a product, etc. There is appreciable value in integrating the data silos across various industries.

Entity Matching for Digital World: A Modern Approach using Artificial Intelligence and Machine Learning

Entity matching is the field of research solving the problem of identifying similar records which refer to the same real-world entity. In today’s digital world, business organizations deal with large amount of data like customers, vendors, manufacturers, etc. Entities are spread across various data sources and failure to correlate two records as one entity can lead to confusion. Relationships and patterns would be missed. Aggregations and calculations won’t make any sense. It is a significant data integration effort that often arises when data originate from different sources. In such scenarios, we understand the situation by linking records and then track entities from a person to a product, etc. There is appreciable value in integrating the data silos across various industries.

Language: English

K. Victor Rajan
K. Victor Rajan Atlantic International University
Edward Lambert
Edward Lambert

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1. Patel A.Malinovska L.Saha S.Wang J.Alberti S.Krishnan Y.Hyman A. A.2017ATP as a Biological HydrotropeScience 356:753–756

2. Sridharan S.Kurzawa N.Werner T.Günthner I.Helm D.Huber W.Bantscheff M.Savitski M. M.2019Proteome-Wide Solubility and Thermal Stability Profiling Reveals Distinct Regulatory Roles for ATPNat. Commun 10

K. Victor Rajan. 2026. “. Global Journal of Computer Science and Technology – D: Neural & AI GJCST-D Volume 23 (GJCST Volume 23 Issue D1): .

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Issue Cover
GJCST Volume 23 Issue D1
Pg. 35- 44
Classification
GJCST-D Classification: FOR Code: 170203
Keywords
Article Matrices
Total Views: 2291
Total Downloads: 36
2026 Trends
Research Identity (RIN)
PMC6579259
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Entity Matching for Digital World: A Modern Approach using Artificial Intelligence and Machine Learning

K. Victor Rajan
K. Victor Rajan Atlantic International University
Edward Lambert
Edward Lambert

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