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Mathematical Similarity Models: Do We Need Incomparability to Be Precise?

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Graph-Based Representation and Reasoning (ICCS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11530))

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Abstract

The expanded version of Tversky’s feature model by Geist, Lengnink and Wille (GLW) provides a compelling representation of similarity by adding an incomparability option. However, previous research has found the GLW model lacking when compared with participants’ responses. We present several new models based on the GLW model and validate them on data from previous similarity experiments. The results indicate that human similarity perception is not well described by a partial order.

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References

  1. Endres, D., Adam, R., Giese, M.A., Noppeney, U.: Understanding the semantic structure of human fMRI brain recordings with formal concept analysis. In: Domenach, F., Ignatov, D.I., Poelmans, J. (eds.) ICFCA 2012. LNCS (LNAI), vol. 7278, pp. 96–111. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29892-9_13

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  2. Geist, S., Lengnink, K., Wille, R.: An order-theoretic foundation for similarity measures. In: Lengnink, K. (ed.) Formalisierungen von Ähnlichkeit aus Sicht der Formalen Begriffsanalyse, pp. 75–87. Shaker Verlag (1996)

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  3. Schubert, M., Endres, D.: Empirically evaluating the similarity model of Geist, Lengnink and Wille. In: Chapman, P., Endres, D., Pernelle, N. (eds.) ICCS 2018. LNCS (LNAI), vol. 10872, pp. 88–95. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91379-7_7

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  4. Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327–352 (1977). https://doi.org/10.1037/0033-295X.84.4.327

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Acknowledgement

This work was supported by the DFG SFB/TRR 135 “Cardinal Mechanisms of Perception”, project C6.

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Correspondence to Dominik Endres .

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Serr, A., Schubert, M., Endres, D. (2019). Mathematical Similarity Models: Do We Need Incomparability to Be Precise?. In: Endres, D., Alam, M., Ĺžotropa, D. (eds) Graph-Based Representation and Reasoning. ICCS 2019. Lecture Notes in Computer Science(), vol 11530. Springer, Cham. https://doi.org/10.1007/978-3-030-23182-8_20

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  • DOI: https://doi.org/10.1007/978-3-030-23182-8_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23181-1

  • Online ISBN: 978-3-030-23182-8

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