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Attribute Exploration with Multiple Contradicting Partial Experts

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Graph-Based Representation and Reasoning (ICCS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13403))

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Abstract

Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a group of domain experts and explores their shared views. Each expert has their own view of the domain and the views of multiple experts may contain contradicting information.

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Notes

  1. 1.

    https://doi.org/10.48550/arXiv.2205.15714.

  2. 2.

    For modeling incomplete information, we later extend \(\{\times ,o\}\) by “?”, cf. Definition 2.1.

  3. 3.

    Note that we consider \(\mathcal {L}\) an implication theory because it is easier to work with. In practice we can use any set of implications where the closure is satisfiable in \(\mathbb {K}\).

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Author information

Authors and Affiliations

  1. Knowledge and Data Engineering Group, University of Kassel, Kassel, Germany

    Maximilian Felde & Gerd Stumme

  2. Interdisciplinary Research Center for Information System Design, University of Kassel, Kassel, Germany

    Maximilian Felde & Gerd Stumme

Authors
  1. Maximilian Felde
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  2. Gerd Stumme
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Corresponding author

Correspondence to Maximilian Felde .

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Editors and Affiliations

  1. University of Münster, Münster, Germany

    Tanya Braun

  2. Babes-Bolyai University, Cluj-Napoca, Romania

    Diana Cristea

  3. Humboldt-Universität zu Berlin, Berlin, Germany

    Robert Jäschke

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Felde, M., Stumme, G. (2022). Attribute Exploration with Multiple Contradicting Partial Experts. In: Braun, T., Cristea, D., Jäschke, R. (eds) Graph-Based Representation and Reasoning. ICCS 2022. Lecture Notes in Computer Science(), vol 13403. Springer, Cham. https://doi.org/10.1007/978-3-031-16663-1_5

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  • DOI: https://doi.org/10.1007/978-3-031-16663-1_5

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  • Print ISBN: 978-3-031-16662-4

  • Online ISBN: 978-3-031-16663-1

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