Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning

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.

The odyssey to next-generation computers: cognitive computers (κC) inspired by the brain and powered by intelligent mathematics

“…On the basis of semantic algebra, semantic expressions may not only be deductively analyzed based on their syntactic structures from the top down, but also be synthetically composed by the algebraic semantic operators from the bottom up. Semantic algebra has enabled a wide range of applications in cognitive informatics, cognitive linguistics, cognitive computing, machine learning, cognitive robots, as well as natural language analysis, synthesis, and comprehension (Wang et al, 2016a).…”

“…The cognitive informatics model of language comprehension is a cognitive process according to the LRMB and OAR models of internal knowledge representation (Wang, 2007;Wang et al, 2011Wang et al, , 2016a. A rigorous description of the cognitive process of comprehension has been formally described in Wang (2009bWang ( , 2021b.…”

The Cognitive Machine as Mental Language Automata

“…Knowledgelearningisthemostfundamentalcategorytosimulatebraininmachinelearning. Alongtheselines,theEICAengineeringmodelassumesthatthesemanticspaceofknowledgeis amicrogeneticepistemicnetworkofhierarchicalconcept,whichcanberigorouslyrepresentedby formalconceptscharacteristicofthesemanticstructureofnarrativediscourse.Thisarticlepresents theoriesandalgorithmstoclassifyahierarchicalmicrogeneticstatemachinethroughqualitativeand quantitativesemanticanalysisbasedonartificialintelligencetoinstallthelearningcompetenceto observethecognitive-linguisticactivityofthehumanbeing.Thesemanticequivalencebetweenthe formalconceptsofeachEICAmicrogeneticstateisrigorouslymeasuredbyasemantichierarchy thatisquantitativelydeterminedbyaRelationalSemanticClassificationAlgorithm(ARSC) Wang (2016b).TheapplicationoftheEICAmodelevidencesadeepunderstandingofthemicrogenetic statesmachineandtheirrelationshipsinhierarchicalsemanticdiscursivenarrativespacethroughthe learningofmachinestocapturedirectlyunobservableevents,aswellastheperspectivesofempirical observationsofhumanlogical-grammaticalprocessingandcognition.…”

Abstract Intelligence

“…IEEE ICCI*CC'16 on Cognitive Informatics and Cognitive Computing has been held at Stanford University during Aug. [22][23]2016. The theme of ICCI*CC'16 was on cognitive computing, big data cognition, and machine learning (Widrow, 2016;Zadeh, 2016;Wang et al, 2016b).…”