We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Lifted graphical models: a survey.
- Authors
Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise
- Abstract
Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.
- Subjects
GRAPHICAL modeling (Statistics); MACHINE learning; INDUCTIVE logic programming; DATA mining; COMPUTER algorithms; PROBABILISTIC inference
- Publication
Machine Learning, 2015, Vol 99, Issue 1, p1
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1007/s10994-014-5443-2