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Data Graphs with Incomplete Information (and a Way to Complete Them)

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Logics in Artificial Intelligence (JELIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14281))

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Abstract

We introduce a modal language for reasoning about data graphs with incomplete information. Such data graphs are formally represented as models in which data value functions are partial—to capture what is unknown. In this setting, we also allow for unknown data values to be learned. Our main result is a sound and strongly complete axiomatization for the logic.

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Acknowledgments

We thank the reviewers for their valuable comments. Our work is supported by the Laboratoire International Associé SINFIN, the EU Grant Agreement 101008233 (MISSION), the ANPCyT projects PICT-2020-3780, PICT-2021-00400, PICT-2021-00675, and PICTO-2022-CBA-00088, and the CONICET projects PIBAA-28720210100428CO, PIBAA-28720210100165CO, and PIP-11220200100812CO.

Author information

Authors and Affiliations

  1. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Carlos Areces, Valentin Cassano, Danae Dutto & Raul Fervari

  2. Universidad Nacional de Córdoba (UNC), Córdoba, Argentina

    Carlos Areces, Valentin Cassano, Danae Dutto & Raul Fervari

  3. Universidad Nacional de Río Cuarto (UNRC), Río Cuarto, Argentina

    Valentin Cassano & Danae Dutto

  4. Guangdong Technion - Israel Institute of Technology (GTIIT), Shantou, China

    Raul Fervari

Authors
  1. Carlos Areces
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  2. Valentin Cassano
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  3. Danae Dutto
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  4. Raul Fervari
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Corresponding author

Correspondence to Valentin Cassano .

Editor information

Editors and Affiliations

  1. TU Dresden, Dresden, Germany

    Sarah Gaggl

  2. Artificial Intelligence Research Institute - IIIA CSIC, Barcelona, Spain

    Maria Vanina Martinez

  3. Umeå University, Umeå, Sweden

    Magdalena Ortiz

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Areces, C., Cassano, V., Dutto, D., Fervari, R. (2023). Data Graphs with Incomplete Information (and a Way to Complete Them). In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_49

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  • DOI: https://doi.org/10.1007/978-3-031-43619-2_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43618-5

  • Online ISBN: 978-3-031-43619-2

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