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    Dimensions of Commonsense Knowledge

    Author
    Ilievski, Filip; Oltramari, Alessandro; Ma, Kaixin; Zhang, Bin; McGuinness, Deborah; Szekely, Pedro
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    Date Issued
    2021-10-11
    Subject
    Machine Common Sense (MCS) Multi-modal Open World Grounded Learning and Inference (MOWGLI)
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    Terms of Use
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    URI
    https://www.sciencedirect.com/science/article/abs/pii/S0950705121006092?via%3Dihub=
    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 allows us to unify the separate sources and to compute indications of their coverage, overlap, and gaps with respect to the knowledge dimensions. Moreover, we analyze the impact of each dimension on downstream reasoning tasks that require commonsense knowledge, observing that the temporal and desire/goal dimensions are very beneficial for reasoning on current downstream tasks, while distinctness and lexical knowledge have little impact. These results reveal preferences for some dimensions in current evaluation, and potential neglect of others.;
    Department
    Publisher
    Knowledge-Based Systems
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    https://tw.rpi.edu/project/machine-common-sense-mcs-multi-modal-open-world-grounded-learning-and-inference-mowgli;
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