Learning Bayesian Networks from the Knowledge of a Never-Ending Learning System



Bayesian networks, never-ending learning, machine learning


Never-Ending Learning (NEL) is a new paradigm of machine learning in which computer systems can learn continuously and incrementally. The first never-ending learning system reported in literature is named NELL (Never-Ending Language Learning). NELL has built a Knowledge Base (KB) containing many types of knowledge. In this paper, we propose to learn Bayesian networks from the knowledge stored in the NELL’s KB aiming to assist in the development of expert systems in the future. In addition, Bayesian networks have shown to be promising to solve the problem of representing semantic relations and extending the NELL’s initial ontology. In the initial experiments, we have built a dataset on the domain of diseases from relations existing in NELL and applied two learning algorithms of Bayesian networks named K2 and DMBC. The empirical results have shown that this proposal is promising.


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Author Biographies

Rogers R. Avelar de Carvalho, Federal University of Sao Joao del Rei, Brazil

Master from the Federal University of Sao Joao del-Rei in Computer Science (2021). He has 15 years experience in the technology market. Acting as Systems Analyst, Data Administrator, BI Analyst and Software Engineer. Experience as a teacher in technical, technological and higher education.

Edimilson Batista dos Santos, Universidade Federal de São João del-Rei

Graduated in Computer Science from the Federal University of Lavras/MG (2003), Master in Computer Science from the Federal University of Sao Carlos/SP (2007) and Ph.D. in High Performance Computational Systems from the Federal University of Rio de Janeiro (PEC/COPPE/UFRJ) (2011). He is currently professor at the Department of Computer Science at the Federal University of Sao Joao del Rei/MG (UFSJ). He has experience in Computer Science, with emphasis on Machine Learning, Data Mining and working mainly on the following topics: machine learning, never-ending learning, Bayesian models, evolutionary algorithms and intelligent systems.


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How to Cite

Rogers R. Avelar de Carvalho, & Batista dos Santos, E. (2022). Learning Bayesian Networks from the Knowledge of a Never-Ending Learning System. IEEE Latin America Transactions, 100(XXX). Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6446