Abstract
Commonsense Reasoning is a subfield of artificial intelligence that deals with ability of a computer to imitate mundane decision making. Though the field existed from decades, there has been very little contribution made to the development of commonsense reasoning. Lack of a well-defined methodology as well as computing facilities for implementing commonsense reasoning have always been obstacles in the development process. Semantic networks can be used to conceptualize and implement a part of commonsense reasoning. This paper presents a study on performance measurement and analysis of one such model semantic network used to build commonsense knowledge-base. Various categories of performance measures have been presented to analyze the practicality of such models for automation of commonsense reasoning. Overall, the analysis is intended to present a practical feasibility study of a model semantic network by considering characteristics of a commonsense knowledge-base.
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Hegde, C., Ashwini, K. (2021). Measuring the Performance of a Model Semantic Knowledge-Base for Automation of Commonsense Reasoning. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_22
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DOI: https://doi.org/10.1007/978-981-33-6081-5_22
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