Structure learning of probabilistic relational models from incomplete relational data

Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn structures of probabilistic relational model (PRM) from incomplete relational data. The missing values are filled in randomly at first, and a maximum likelihood tree (MLT) is generated from the complete data sample. Then, Gibbs sampling is combined with MLT to modify the data and regulate MLT iteratively for obtaining a well-completed data set. Finally, probabilistic structure is learned through dependency analysis from the completed data set. Experiments show that the MGDA approach can learn good structures from incomplete relational data.