Formal Ontology Generation by deep machine learning

Wang, Y.; Valipour, M.; Zatarain, O.D.; Gavrilova, M.L.; Hussain, A.; Howard, N.; Patel, S.

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.

Citation

Wang, Y., Valipour, M., Zatarain, O., Gavrilova, M., Hussain, A., Howard, N., & Patel, S. (2017, July). Formal Ontology Generation by deep machine learning. Presented at 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
• Start Date: Jul 26, 2017
• End Date: Jul 28, 2017
• Online Publication Date: Nov 16, 2017
• Publication Date: Nov 16, 2017
• Deposit Date: Sep 23, 2019
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 6-15
• DOI: https://doi.org/10.1109/ICCI-CC.2017.8109723
• Keywords: Ontology, formal models, autonomic generation, concept algebra, machine learning, knowledge representation, cognitive robot, denotational semantics, cognitive computing, AI, computational intelligence
• Public URL: http://researchrepository.napier.ac.uk/Output/1792490