This paper presents an overview of the research on learning statistical models from relational data being carried out at the University of Washington. Our work falls into five main directions: learning models of social networks; learning models of sequential relational processes; scaling up statistical relational learning to massive data sources; learning for knowledge integration; and learning programs in procedural languages. We describe some of the common themes and research issues arising from this work.(local copy)
Published in the proceedings of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data part of IJCAI-2003.