Dimensions of Commonsense Knowledge
Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L., McGuinness, Pedro Szekely

TL;DR
This paper organizes various commonsense knowledge sources into 13 key dimensions, analyzing their coverage, overlaps, and impact on AI reasoning tasks to identify strengths and gaps in current resources.
Contribution
It introduces a unified framework of 13 knowledge dimensions for commonsense sources, enabling better comparison and understanding of their coverage and relevance.
Findings
Temporal and desire/goal dimensions significantly improve reasoning tasks.
Distinctness and lexical knowledge have minimal impact on reasoning.
Many sources lack comprehensive coverage of all key dimensions.
Abstract
Commonsense knowledge is essential for many AI applications, including those in natural language processing, visual processing, and planning. Consequently, many sources that include commonsense knowledge have been designed and constructed over the past decades. Recently, the focus has been on large text-based sources, which facilitate easier integration with neural (language) models and application to textual tasks, typically at the expense of the semantics of the sources and their harmonization. Efforts to consolidate commonsense knowledge have yielded partial success, with no clear path towards a comprehensive solution. We aim to organize these sources around a common set of dimensions of commonsense knowledge. We survey a wide range of popular commonsense sources with a special focus on their relations. We consolidate these relations into 13 knowledge dimensions. This consolidation…
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