A cognitive model of human bias in matching

Rakefet Ackerman, Avigdor Gal, Tomer Sagi, Roee Shraga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The schema matching problem is at the basis of integrating structured and semi-structured data. Being investigated in the fields of databases, AI, semantic Web and data mining for many years, the core challenge still remains the ability to create quality matchers, automatic tools for identifying correspondences among data concepts (e.g., database attributes). In this work, we investigate human matchers behavior using a new concept termed match consistency and introduce a novel use of cognitive models to explain human matcher performance. Using empirical evidence, we further show that human matching suffers from predictable biases when matching schemata, which prevent them from providing consistent matching.

Original languageEnglish
Title of host publicationPRICAI 2019
Subtitle of host publicationTrends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsAbhaya C. Nayak, Alok Sharma
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783030299071
StatePublished - 2019
Event16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019 - Yanuka Island, Fiji
Duration: 26 Aug 201930 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11670 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019
CityYanuka Island

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • Data integration
  • Human-in-the-loop
  • Schema matching

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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