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Large-Scale Commonsense Knowledge for Default Logic Reasoning

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Abstract

Commonsense reasoning (CSR) is the ability to reason about everyday situations. In artificial intelligence systems, such reasoning requires extensive background knowledge. We implement an integration pipeline that transforms existing large commonsense knowledge (CSK) resources into a logic representation designed to support contradictory and uncertain information. Our representation is an extension of first-order logic with default rules and numeric confidences. Using the pipeline, we integrate eight large CSK graphs to build a large knowledge base for symbolic hybrid AI. To the best of our knowledge, our contribution is the first large-scale commonsense knowledge base using default logic, available online. The CSK sources complement each other, having 5% or less overlap in knowledge between any pair of input graphs. The integrated knowledge supports taxonomy related queries. Significant gaps in commonsense knowledge remain, because most of the CSK sources lack rules that enable further reasoning beyond taxonomy.

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Notes

  1. Credit for the example goes to Dr. Pavel Smirnov from Honda Research Institute.

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  1. Applied Artificial Intelligence Group, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Priit Järv, Tanel Tammet & Martin Verrev

  2. Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, 12618, Tallinn, Estonia

    Dirk Draheim

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This article is part of the topical collection “Advances on Knowledge Discovery, Knowledge Engineering and Knowledge Management” guest edited by Joaquim Filipe, Ana Fred, Frans Coenen, Jorge Bernardino and Elio Masciari.

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Järv, P., Tammet, T., Verrev, M. et al. Large-Scale Commonsense Knowledge for Default Logic Reasoning. SN COMPUT. SCI. 4, 550 (2023). https://doi.org/10.1007/s42979-023-01963-2

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