Preface

Manfred Jaeger, Lise Getoor, Kristian Kersting
2008 Annals of Mathematics and Artificial Intelligence  
Probabilistic Relational Learning is a research area that brings together several previously separated lines of research. One line of research originates with early approaches to combine probabilistic graphical models with higher-level, logic-based representation languages. Originally mostly pursued for the purpose of knowledge representation and reasoning (and then called knowledge-based model construction), this line of research gained significant momentum when its focus shifted to machine
more » ... rning, where it was found that these new representation languages are wellsuited to provide statistical models for relational data. A second line of research is represented by inductive logic programming, which for a long time had been concerned with learning purely logical models from logical or relational data, and over the past decade has increasingly turned towards probabilistic-logic models as well. Finally, over the past few years a further integration has taken place with subareas of machine learning that are concerned with learning from structured data, notably graph mining. The vitality of the emerging field is manifested by numerous international workshops and seminars, notably the sequence of workshops on Statistical Relational
doi:10.1007/s10472-009-9131-z fatcat:qpgckbzb6fhvjllssxivlwlsvq