Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning

Yingxu Wang, Mehrdad Valipour and Omar A. Zatarain
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Yingxu Wang: International Institute of Cognitive Informatics and Cognitive Computing (ICIC), Laboratory for Computational Intelligence, Cognitive Systems, Software Science, and Denotational Mathematics, Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
Mehrdad Valipour: International Institute of Cognitive Informatics and Cognitive Computing (ICIC), Laboratory for Computational Intelligence, Cognitive Systems, Software Science, and Denotational Mathematics, Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
Omar A. Zatarain: International Institute of Cognitive Informatics and Cognitive Computing (ICIC), Laboratory for Computational Intelligence, Cognitive Systems, Software Science, and Denotational Mathematics, Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2016, vol. 10, issue 3, 13-28

Abstract: Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network (HCN), which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis (ACEA). The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification (ARSC). Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.

Date: 2016
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