(Artificial) Mind over matter: Humans in and humans out in matching

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Abstract

The matching task is at the heart of data integration, in charge of aligning elements of data sources. Historically, matching problems were considered semi automated tasks in which correspondences are generated by matching algorithms and subsequently validated by human expert(s). This research is devoted to the changing role of humans in matching, which is divided into two main approaches, namely Humans Out and Humans In. With the increase in amount and size of matching tasks, the role of humans as validators seems to diminish; thus Humans In questions the inherent need for humans in the matching loop. On the other hand, Humans Out focuses on overcoming human cognitive biases via algorithmic assistance. Above all, we observe that matching requires unconventional thinking demonstrated by advance machine learning methods to complement (and possibly take over) the role of humans in matching.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2652
StatePublished - 2020
Event2020 International Conference on Very Large Databases PhD Workshop, VLDB-PhD 2020 - Virtual, Online, Japan
Duration: 31 Aug 20204 Sep 2020

All Science Journal Classification (ASJC) codes

  • General Computer Science

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