Formal Ontology Generation by deep machine learning

Yingxu Wang; Mehrdad Valipour; Omar D. Zatarain; Marina L. Gavrilova; Amir Hussain; Newton Howard; Shushma Patel

doi:10.1109/ICCI-CC.2017.8109723

Formal Ontology Generation by deep machine learning

Yingxu Wang
, Mehrdad Valipour
, Omar D. Zatarain
, Marina L. Gavrilova
, Amir Hussain
, Newton Howard
, Shushma Patel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017
Editors	Yingxu Wang, Freddie Hamdy, Newton Howard, Lotfi A. Zadeh, Amir Hussain, Bernard Widrow
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6-15
Number of pages	10
ISBN (Electronic)	9781538607701
DOIs	https://doi.org/10.1109/ICCI-CC.2017.8109723
State	Published - 14 Nov 2017
Externally published	Yes
Event	16th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017 - Oxford, United Kingdom Duration: 26 Jul 2017 → 28 Jul 2017

Publication series

Name	Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017

Conference

Conference	16th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017
Country/Territory	United Kingdom
City	Oxford
Period	26/07/17 → 28/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

AI
Ontology
autonomic generation
cognitive computing
cognitive robot
computational intelligence
concept algebra
denotational semantics
formal models
knowledge representation
machine learning

ASJC Scopus subject areas

Cognitive Neuroscience
Information Systems
Computer Science (miscellaneous)

Access to Document

10.1109/ICCI-CC.2017.8109723

Cite this

Wang, Y., Valipour, M., Zatarain, O. D., Gavrilova, M. L., Hussain, A., Howard, N., & Patel, S. (2017). Formal Ontology Generation by deep machine learning. In Y. Wang, F. Hamdy, N. Howard, L. A. Zadeh, A. Hussain, & B. Widrow (Eds.), Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017 (pp. 6-15). Article 8109723 (Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCI-CC.2017.8109723

Wang, Yingxu ; Valipour, Mehrdad ; Zatarain, Omar D. et al. / Formal Ontology Generation by deep machine learning. Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017. editor / Yingxu Wang ; Freddie Hamdy ; Newton Howard ; Lotfi A. Zadeh ; Amir Hussain ; Bernard Widrow. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 6-15 (Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017).

@inproceedings{1d2ef141e26a4d5297c0bd9357acd662,

title = "Formal Ontology Generation by deep machine learning",

abstract = "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.",

keywords = "AI, Ontology, autonomic generation, cognitive computing, cognitive robot, computational intelligence, concept algebra, denotational semantics, formal models, knowledge representation, machine learning",

author = "Yingxu Wang and Mehrdad Valipour and Zatarain, \{Omar D.\} and Gavrilova, \{Marina L.\} and Amir Hussain and Newton Howard and Shushma Patel",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",

year = "2017",

month = nov,

day = "14",

doi = "10.1109/ICCI-CC.2017.8109723",

language = "English",

series = "Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6--15",

editor = "Yingxu Wang and Freddie Hamdy and Newton Howard and Zadeh, \{Lotfi A.\} and Amir Hussain and Bernard Widrow",

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Wang, Y, Valipour, M, Zatarain, OD, Gavrilova, ML, Hussain, A, Howard, N & Patel, S 2017, Formal Ontology Generation by deep machine learning. in Y Wang, F Hamdy, N Howard, LA Zadeh, A Hussain & B Widrow (eds), Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017., 8109723, Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 6-15, 16th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017, Oxford, United Kingdom, 26/07/17. https://doi.org/10.1109/ICCI-CC.2017.8109723

Formal Ontology Generation by deep machine learning. / Wang, Yingxu; Valipour, Mehrdad; Zatarain, Omar D. et al.
Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017. ed. / Yingxu Wang; Freddie Hamdy; Newton Howard; Lotfi A. Zadeh; Amir Hussain; Bernard Widrow. Institute of Electrical and Electronics Engineers Inc., 2017. p. 6-15 8109723 (Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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ER -

Wang Y, Valipour M, Zatarain OD, Gavrilova ML, Hussain A, Howard N et al. Formal Ontology Generation by deep machine learning. In Wang Y, Hamdy F, Howard N, Zadeh LA, Hussain A, Widrow B, editors, Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 6-15. 8109723. (Proceedings of 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017). doi: 10.1109/ICCI-CC.2017.8109723

Formal Ontology Generation by deep machine learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this