NDLI: Lifted graphical models: a survey

Content Provider	Springer Nature Link
Author	Kimmig, Angelika Mihalkova, Lilyana Getoor, Lise
Copyright Year	2014
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.
Starting Page	1
Ending Page	45
Page Count	45
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	99
Issue Number	1
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2014-05-22
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Statistical relational learning First-order probabilistic models Probabilistic programming Par-factor graphs Templated graphical models Lifted inference and learning Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Computing Methodologies Simulation and Modeling Language Translation and Linguistics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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