{"title":"From computing with words (CWW) to reasoning with fuzzy concepts (RFC)","authors":"Yingxu Wang","doi":"10.1109/NAFIPS.2016.7851607","DOIUrl":null,"url":null,"abstract":"The fuzzy nature of language structures and semantics is formally studied towards a methodology for reasoning with fuzzy concepts (RFC). Mathematical models of fuzzy concepts and fuzzy semantics are introduced based on concept algebra and semantic algebra. The semantic effects of fuzzy modifiers and quantifiers on fuzzy concepts are quantitatively analyzed. Experiments on collective intension and extension elicitation for formal concepts demonstrate that fuzziness of human knowledge stem from the cognitive complexity, inexplicitness, subjectivity, diversity, redundancy, incompleteness, mixed synonyms, informal representation, incoherent attributes, divergent objects, and contextual influence. The RFC methodology provides a formal approach to computing with words (CW) for cognitive robots, deep machine learning, and fuzzy systems to rigorously manipulate fuzzy language entities, semantics, and reasoning in a wide range of applications.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2016.7851607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The fuzzy nature of language structures and semantics is formally studied towards a methodology for reasoning with fuzzy concepts (RFC). Mathematical models of fuzzy concepts and fuzzy semantics are introduced based on concept algebra and semantic algebra. The semantic effects of fuzzy modifiers and quantifiers on fuzzy concepts are quantitatively analyzed. Experiments on collective intension and extension elicitation for formal concepts demonstrate that fuzziness of human knowledge stem from the cognitive complexity, inexplicitness, subjectivity, diversity, redundancy, incompleteness, mixed synonyms, informal representation, incoherent attributes, divergent objects, and contextual influence. The RFC methodology provides a formal approach to computing with words (CW) for cognitive robots, deep machine learning, and fuzzy systems to rigorously manipulate fuzzy language entities, semantics, and reasoning in a wide range of applications.
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