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Associative Reasoning for Commonsense Knowledge

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KI 2023: Advances in Artificial Intelligence (KI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14236))

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Abstract

Associative reasoning refers to the human ability to focus on knowledge that is relevant to a particular problem. In this process, the meaning of symbol names plays an important role: when humans focus on relevant knowledge about the symbol ice, similar symbols like snow also come into focus. In this paper, we model this associative reasoning by introducing a selection strategy that extracts relevant parts from large commonsense knowledge sources. This selection strategy is based on word similarities from word embeddings and is therefore able to take the meaning of symbol names into account. We demonstrate the usefulness of this selection strategy with a case study from creativity testing.

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Authors and Affiliations

  1. Institut für Informatik, Hochschule Trier, Trier, Germany

    Claudia Schon

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  1. Claudia Schon
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Correspondence to Claudia Schon .

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Editors and Affiliations

  1. Universität Würzburg, Würzburg, Germany

    Dietmar Seipel

  2. University of Greifswald, Greifswald, Germany

    Alexander Steen

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Schon, C. (2023). Associative Reasoning for Commonsense Knowledge. In: Seipel, D., Steen, A. (eds) KI 2023: Advances in Artificial Intelligence. KI 2023. Lecture Notes in Computer Science(), vol 14236. Springer, Cham. https://doi.org/10.1007/978-3-031-42608-7_14

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  • DOI: https://doi.org/10.1007/978-3-031-42608-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42607-0

  • Online ISBN: 978-3-031-42608-7

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