Bayesian Networks for Identifying Semantic Relations in a Never-Ending Learning System

Abstract

A new paradigm of Machine Learning named Never-Ending Learning has been proposed through a system known as NELL (Never-Ending Language Learning). The major idea of this system is to learn to read the web better each day and to store the gathered knowledge in a knowledge base (KB), continually and incrementally. This paper proposes a new method that can help NELL populating its own KB using Bayesian Networks (BN). More specifically, we use facts (knowledge) already stored in NELL’s KB as input for a BN learning algorithm named VOMOS (Variable Ordering Multiple Offspring Sampling) by aiming at representing the acquired knowledge by NELL system. In addition, we propose to use the BN induced by VOMOS for identifying new semantic relations to be added to NELL’s KB, expanding thus its initial ontology.