Theories and Experiments of Cognitive Knowledge Bases for Machine Learning
AuthorZatarain Duran, Omar Ali
Committee MemberGavrilova, Marina L.
Fapojuwo, Abraham Olatunji
Subjectcognitive machine learning
cognitive knowledge bases
MetadataShow full item record
AbstractThis thesis presents a framework of studies on theories, methodologies, algorithms, and experiments on cognitive knowledge bases (CKBs) for machine knowledge learning in cognitive computing and computational linguistics. CKB is both the results and the means of machine learning methodologies mimicking human learning and semantic comprehensions. Technologies for machine learning can be classified into six categories according to Dr. Y. Wang known as object identification, cluster classification, pattern recognition, functional regression, behavior generation, and knowledge acquisition. Most current machine learning techniques fall into the first five categories. However, the sixth category of knowledge learning as humans do has remained as a fundamental problem and challenge in machine learning, AI, and computational intelligence. A set of algorithms, tools, and experiments on machine knowledge learning is designed in order to demonstrate that cognitive machines may create their own concepts and CKBs through knowledge learning. The accuracy and cohesiveness of machine learnt results may outperform humans. This leads to the implementation of formal knowledge comprehension and quantitative semantic analyses by cognitive systems based on CKBs and machine semantic comprehensions. The theoretical framework and case studies derived from this research will impact the field of machine knowledge learning technologies and the development of novel cognitive systems. This research will enable industrial applications such as personal leaning assistants, cognitive search engines, and cognitive translators.
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