Abstract
More and more industrial applications are facing the problem of imprecise information handling. Computing with Words (CW) is a mathematical model for approximate knowledge representation, reasoning, and processing of natural language. The very basic idea of CW is to use words instead of numbers for computing and reasoning using fuzzy sets and logic. However, the implementation of this approach in the project requires certain knowledge. This paper presents our initial efforts towards building of a methodology and library based on the extended version of CW, CWiPy. CWiPy can be effectively used to apply CW techniques easily without any prior knowledge in this field. So, developers can add it to an existing system and use it as a black box (plug and play). In CWiPy, the traditional CW was extended to process a bigger variety of linguistic hedges, enhancing the system expressiveness. CWiPy provides an API that allows handling of fuzzy variables, sets, hedges, and quantifiers. Results show that CWiPY can be easily applied in real-life industrial applications to deal with imprecise information and provide help for experts. Two different usage scenarios of the library are presented as a proof of concept: natural language query processing and database summarization .
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Notes
- 1.
Dataset “Supermarket sales. Historical record of sales data in 3 different supermarkets.” (https://www.kaggle.com/aungpyaeap/supermarket-sales).
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Kali, A., Shamoi, P., Zhangbyrbayev, Y., Zhandaulet, A. (2023). Computing with Words for Industrial Applications. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_17
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