Fundamental ideas and mathematical basis of ontology learning algorithm

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dc.contributor.authorZhu, Linli
dc.contributor.authorHua, Gang
dc.contributor.authorSohail Zafar
dc.contributor.authorPan, Yu
dc.date.accessioned2019-04-26T11:06:18Z
dc.date.available2019-04-26T11:06:18Z
dc.date.issued2018
dc.description.abstractAs a data utility and aided tool, ontology has been widely used in many areas of the computer. Owing to its great efficiency, ontologies have also been introduced into various engineering disciplines. In this paper, we present the fundamental ideas of how to deal with similarity measuring problem in ontology learning algorithms. The mathematical basis of ontology learning algorithms is also introduced from a statistical learning theory point of view. Finally, we present two ontology learning algorithms in multi-dividing setting and ontology sparse vector learning setting, respectively.en_US
dc.identifier.citationZhu, L., Hua, G., Zafar, S., & Pan, Y. (2018). Fundamental ideas and mathematical basis of ontology learning algorithm. Journal of Intelligent & Fuzzy Systems(Preprint), 1-14. (Sohail Zafar, (Mathematics), (SJR Listed))en_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/3740
dc.language.isoenen_US
dc.publisherJournal of Intelligent & Fuzzy Systems(Preprint)en_US
dc.subjectontology, similarity measuring, graph model, machine learning, multi-dividing settingen_US
dc.titleFundamental ideas and mathematical basis of ontology learning algorithmen_US
dc.typeArticleen_US
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