From Relational Databases to Belief Networks
This work addresses the challenge of integrating statistical relational data into belief networks for researchers in AI and data science, but it appears incremental as it builds on known relationships between databases and networks.
The paper tackles the problem of constructing belief networks from relational databases, proposing an automated method that shows advantages in generalization and prediction compared to existing approaches.
The relationship between belief networks and relational databases is examined. Based on this analysis, a method to construct belief networks automatically from statistical relational data is proposed. A comparison between our method and other methods shows that our method has several advantages when generalization or prediction is deeded.