Abstract
It is widely accepted that an organization's success is increasingly dependent on its ability to derive value from the data it has. However, many organizations are still stuck on the first step – understanding the data – especially as the volume and complexity of data continue to grow. Think about a simple question: How many customers does your enterprise have? For many businesses, providing an accurate answer to this question remains difficult.
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Notes
- 1.
See Reference [1] for more information on industry use cases.
- 2.
See Reference [2] for more details on the definition of entity matching.
- 3.
See References [5], [6], and [7] for more on entity resolution.
- 4.
See Reference [3] for more details on schema matching.
- 5.
See Reference [4] for more on blocking techniques.
- 6.
See Reference [4] for a blocking algorithms survey.
- 7.
See Reference [8] for more on Jaccard indexes.
- 8.
See Reference [9] for more on applying neural networks for entity matching.
- 9.
See Reference [10] for more information on Recurrent Neural Networks.
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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). AI for Entity Resolution. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9253-2_8
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DOI: https://doi.org/10.1007/978-1-4842-9253-2_8
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