caa a22 4500 605478449 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s10994-014-5443-2 doi (NATIONALLICENCE)springer-10.1007/s10994-014-5443-2 Lifted graphical models: a survey [Elektronische Daten] [Angelika Kimmig, Lilyana Mihalkova, Lise Getoor] 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. The Author(s), 2014 Statistical relational learning nationallicence First-order probabilistic models nationallicence Probabilistic programming nationallicence Par-factor graphs nationallicence Templated graphical models nationallicence Lifted inference and learning nationallicence Kimmig Angelika Department of Computer Science, KU Leuven, Leuven, Belgium aut Mihalkova Lilyana Google, 340 Main Street, Los Angeles, CA, USA aut Getoor Lise Computer Science Department, University of California, Santa Cruz, CA, USA aut Machine Learning Springer US; http://www.springer-ny.com 99/1(2015-04-01), 1-45 0885-6125 99:1<1 2015 99 10994 https://doi.org/10.1007/s10994-014-5443-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5443-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kimmig Angelika Department of Computer Science, KU Leuven, Leuven, Belgium aut NATIONALLICENCE 700 1- Mihalkova Lilyana Google, 340 Main Street, Los Angeles, CA, USA aut NATIONALLICENCE 700 1- Getoor Lise Computer Science Department, University of California, Santa Cruz, CA, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 99/1(2015-04-01), 1-45 0885-6125 99:1<1 2015 99 10994