Large-Scale Commonsense Knowledge for Default Logic Reasoning

被引：0

作者：

Järv P. ^{[1
]}

Tammet T. ^{[1
]}

Verrev M. ^{[1
]}

Draheim D. ^{[2
]}

机构：

[1] Applied Artificial Intelligence Group, Tallinn University of Technology, Akadeemia tee 15a, Tallinn

[2] Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, Tallinn

来源：

SN Computer Science | / 4卷 / 5期

关键词：

Commonsense reasoning; Default logic; Knowledge base;

D O I：

10.1007/s42979-023-01963-2

中图分类号：

学科分类号：

摘要：

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. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

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