Skip to main content
Book cover

Pacific Rim International Conference on Artificial Intelligence

PRICAI 2019: PRICAI 2019: Trends in Artificial Intelligence pp 632–646Cite as

A Cognitive Model of Human Bias in Matching

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11670)

Abstract

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.

Keywords

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

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-29908-8_50
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-29908-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

(adapted from [1]).

Fig. 4.

Notes

  1. 1.

    www.cise.ufl.edu/research/dbintegrate/thalia/howto.html.

References

  1. Ackerman, R.: The diminishing criterion model for metacognitive regulation of time investment. J. Exp. Psychol.: Gen. 143, 1349 (2014)

    CrossRef  Google Scholar 

  2. Ackerman, R., Thompson, V.: Meta-reasoning: monitoring and control of thinking and reasoning. TiCS 21, 607–617 (2017)

    Google Scholar 

  3. Raykar, V.C., et al.: Supervised learning from multiple experts: whom to trust when everyone lies a bit. In: ICML (2009)

    Google Scholar 

  4. Barsalou, L.W.: Cognitive Psychology: An Overview for Cognitive Scientists. Psychology Press, New York (2014)

    Google Scholar 

  5. Bellahsene, Z., Bonifati, A., Rahm, E. (eds.): Schema Matching and Mapping. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-16518-4

    CrossRef  MATH  Google Scholar 

  6. Bernstein, P.A., Madhavan, J., Rahm, E.: Generic schema matching, ten years later. PVLDB 4, 695–701 (2011)

    Google Scholar 

  7. Bjork, R.A., Dunlosky, J., Kornell, N.: Self-regulated learning: beliefs, techniques, and illusions. Ann. Rev. Psychol. 64, 417–444 (2013)

    CrossRef  Google Scholar 

  8. Bozovic, N., Vassalos, V.: Two phase user driven schema matching. In: ADBIS (2015)

    Google Scholar 

  9. Brewer, N., Wells, G.L.: The confidence-accuracy relationship in eyewitness identification: effects of lineup instructions, foil similarity, and target-absent base rates. J. Exp. Psychol.: Appl. 12, 11 (2006)

    Google Scholar 

  10. De Una, D., Rümmele, N., Gange, G., Schachte, P., Stuckey, P.J.: Machine learning and constraint programming for relational-to-ontology schema mapping. In: IJCAI (2018)

    Google Scholar 

  11. Do, H.H., Rahm, E.: COMA: a system for flexible combination of schema matching approaches. In: VLDB (2002)

    CrossRef  Google Scholar 

  12. Dragisic, Z., Ivanova, V., Lambrix, P., Faria, D., Jiménez-Ruiz, E., Pesquita, C.: User validation in ontology alignment. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 200–217. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46523-4_13

    CrossRef  Google Scholar 

  13. Dunning, D., Heath, C., Suls, J.M.: Flawed self-assessment implications for health, education, and the workplace. Psychol. Sci. Public Interest 5, 69–106 (2004)

    CrossRef  Google Scholar 

  14. Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, New York (2007). https://doi.org/10.1007/978-3-540-49612-0

    CrossRef  MATH  Google Scholar 

  15. Franklin, M.J., Kossmann, D., Kraska, T., Ramesh, S., Xin, R.: CrowdDB: answering queries with crowdsourcing. In: SIGMOD (2011)

    Google Scholar 

  16. Gal, A.: Uncertain Schema Matching. Morgan & Claypool Publishers, San Rafael (2011)

    MATH  Google Scholar 

  17. Gal, A., Roitman, H., Sagi, T.: From diversity-based prediction to better ontology & schema matching. In: WWW (2016)

    Google Scholar 

  18. Gal, A., Roitman, H., Shraga, R.: Heterogeneous data integration by learning to rerank schema matches. In: ICDM (2018)

    Google Scholar 

  19. Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications. J. Am. Stat. Assoc. 49, 732–764 (1954)

    MATH  Google Scholar 

  20. Halevy, A.Y., Madhavan, J.: Corpus-based knowledge representation. In: IJCAI (2003)

    Google Scholar 

  21. Hung, N.Q.V., Nguyen, T.T., Miklós, Z., Aberer, K., Gal, A., Weidlich, M.: Pay-as-you-go reconciliation in schema matching networks. In: ICDE (2014)

    Google Scholar 

  22. Hung, N.Q.V., Tam, N.T., Miklós, Z., Aberer, K.: On leveraging crowdsourcing techniques for schema matching networks. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7826, pp. 139–154. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37450-0_10

    CrossRef  Google Scholar 

  23. Jeffery, S.R., Franklin, M.J., Halevy, A.Y.: Pay-as-you-go user feedback for dataspace systems. In: SIGMOD (2008)

    Google Scholar 

  24. Koriat, A.: Subjective confidence in one’s answers: the consensuality principle. J. Exp. Psychol.: Learn. Memory Cognit. 34, 945–959 (2008)

    Google Scholar 

  25. Koriat, A.: When reality is out of focus: can people tell whether their beliefs and judgments are correct or wrong? J. Exp. Psychol.: Gen. 147, 613 (2018)

    CrossRef  Google Scholar 

  26. McCann, R., Shen, W., Doan, A.: Matching schemas in online communities: a web 2.0 approach. In: ICDE (2008)

    Google Scholar 

  27. Peukert, E., Eberius, J., Rahm, E.: AMC-a framework for modelling and comparing matching systems as matching processes. In: ICDE (2011)

    Google Scholar 

  28. Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDBJ 10, 334–350 (2001)

    CrossRef  Google Scholar 

  29. Sagi, T., Gal, A.: In schema matching, even experts are human. towards expert sourcing in schema matching. In: IIWeb (2014)

    Google Scholar 

  30. Sarasua, C., Simperl, E., Noy, N.F.: CrowdMap: crowdsourcing ontology alignment with microtasks. In: ISWC (2012)

    Google Scholar 

  31. Shraga, R., Gal, A., Roitman, H.: What type of a matcher are you?: coordination of human and algorithmic matchers. In: HILDA@SIGMOD (2018)

    Google Scholar 

  32. Sidi, Y., Shpigelman, M., Zalmanov, H., Ackerman, R.: Understanding metacognitive inferiority on screen by exposing cues for depth of processing. Learn. Instr. 51, 61–73 (2017)

    CrossRef  Google Scholar 

  33. Simonsen, J.C.: Coefficient of variation as a measure of subject effort. Arch. PM&R 76, 516–520 (1995)

    Google Scholar 

  34. Undorf, M., Ackerman, R.: The puzzle of study time allocation for the most challenging items. Psychon. Bull. Rev. 24, 2003–2011 (2017)

    CrossRef  Google Scholar 

  35. Zhang, C., Chen, L., Jagadish, H., Zhang, M., Tong, Y.: Reducing uncertainty of schema matching via crowdsourcing with accuracy rates. TKDE (2018). https://www.computer.org/csdl/journal/tk/5555/01/08533346/17D45XreC6p

Download references

Author information

Authors and Affiliations

  1. Technion – Israel Institute of Technology, Haifa, Israel

    Rakefet Ackerman, Avigdor Gal & Roee Shraga

  2. University of Haifa, Haifa, Israel

    Tomer Sagi

Authors
  1. Rakefet Ackerman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Avigdor Gal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tomer Sagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roee Shraga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roee Shraga .

Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Abhaya C. Nayak

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ackerman, R., Gal, A., Sagi, T., Shraga, R. (2019). A Cognitive Model of Human Bias in Matching. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11670. Springer, Cham. https://doi.org/10.1007/978-3-030-29908-8_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29908-8_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29907-1

  • Online ISBN: 978-3-030-29908-8

  • eBook Packages: Computer ScienceComputer Science (R0)