Formal Ontology Generation by Deep Machine Learning

被引：0

作者：

Wang, Yingxu ^{[1
,2
]}

Valipour, Mehrdad ^{[1
,2
]}

Zatarain, Omar A. ^{[1
,2
]}

Gavrilova, Marina ^{[1
,2
]}

Hussain, Amir ^{[3
]}

Howard, Newton ^{[4
]}

Patel, Shushma ^{[5
]}

机构：

[1] Univ Calgary, Int Inst Cognit Informat & Cognit Comp ICIC, Schulich Sch Engn, 2500 Univ Dr NW, Calgary, AB T2N 1N4, Canada

[2] Univ Calgary, Hotchkiss Brain Inst, 2500 Univ Dr NW, Calgary, AB T2N 1N4, Canada

[3] Univ Stirling, Dept Comp Sci, Stirling, Scotland

[4] Oxford Neurocomp Lab NCL, Oxford, England

[5] London South Bank Univ, Sch Engn, London, England

来源：

2017 IEEE 16TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC) | 2017年

基金：

加拿大自然科学与工程研究理事会;

关键词：

Ontology; formal models; autonomic generation; concept algebra; machine learning; knowledge representation; cognitive robot; denotational semantics; cognitive computing; AI; computational intelligence; DENOTATIONAL MATHEMATICS; CONCEPT ALGEBRA; KNOWLEDGE; SYNTAX;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the latest advances in cognitive knowledge learning and formal semantic analyses, an Algorithm of Formal Ontology Generation (AFOG) is developed. The methodology of AFOG enables autonomous generation of quantitative ontologies in knowledge engineering and semantic comprehension via deep machine learning. A set of experiments demonstrates applications of AFOG in cognitive computing, semantic computing, machine learning and computational intelligence.

引用

页码：6 / 15

页数：10

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