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<journal-id journal-id-type="publisher">global-journal-of-computer-science-and-technology-d-neural-ai</journal-id>
<journal-title-group>
<journal-title>Global Journal of Computer Science and Technology - D: Neural &amp; AI</journal-title>
</journal-title-group>
<issn publication-format="print">0975-4350</issn>
<issn publication-format="electronic">0975-4172</issn>
<publisher><publisher-name>Global Journals Publishing Group Incorporated</publisher-name></publisher>
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<article-id pub-id-type="doi">10.34257/GJCSTDVOL23IS1PG35</article-id>
<article-id pub-id-type="publisher-id">50637</article-id>
<title-group>
<article-title>Entity Matching for Digital World: A Modern Approach using Artificial Intelligence and Machine Learning</article-title>
<subtitle>Supervised Learning for Cross-System Entity Matching</subtitle>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>Rajan</surname><given-names>K. Victor</given-names></name><xref ref-type="aff" rid="aff1" />
</contrib>
<contrib contrib-type="author"><name><surname>Lambert</surname><given-names>Edward</given-names></name></contrib>
</contrib-group>
<aff id="aff1">INDIA, Atlantic International University, USA</aff>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2023-04-10">
<day>10</day>
<month>04</month>
<year>2023</year>
</pub-date>
<volume>23</volume>
<issue>D1</issue>
<fpage>35</fpage>
<lpage>44</lpage>
<abstract><p>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.</p></abstract>
<kwd-group kwd-group-type="author-generated">
<kwd>entity matching</kwd>
<kwd>entity resolution</kwd>
<kwd>record linkage</kwd>
<kwd>de-duplication</kwd>
<kwd>machine learning</kwd>
</kwd-group>
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<title>Full Text</title>
<p>Entity matching is the field of research solving the problem of identifying similar records which refer to the same real-world entity. In todayâ€™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 wonâ€™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.</p>
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