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

Abstract

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 NELLs 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 NELLs 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.