Statistical Relational Learning: Four Claims and a Survey
J. Neville, M. Rattigan, and D. Jensen. Proceedings of the Workshop on Learning Statistical Models from Relational Data, 18th International Joint Conference on Artificial Intelligence., (2003)
Abstract
Statistical relational learning (SRL) research has
made significant progress over the last 5 years.
We have successfully demonstrated the feasibility
of a number of probabilistic models for relational
data, including probabilistic relational
models, Bayesian logic programs, and relational
probability trees, and the interest in SRL is
growing. However, in order to sustain and nurture
the growth of SRL as a subfield we need to
refocus our efforts on the science of machine
learning — moving from demonstrations to
comparative and ablation studies. We will outline
four assertions that are implicit to SRL research
but which have been only minimally
evaluated. We hope to stimulate discussion as to
how, as a community, these claims can be addressed
in future research.
%0 Conference Paper
%1 citeulike:430702
%A Neville, Jennifer
%A Rattigan, Matthew
%A Jensen, David
%B Proceedings of the Workshop on Learning Statistical Models from Relational Data, 18th International Joint Conference on Artificial Intelligence.
%D 2003
%K relationalmodels
%T Statistical Relational Learning: Four Claims and a Survey
%U http://kdl.cs.umass.edu/papers/neville-et-al-srl2003.pdf
%X Statistical relational learning (SRL) research has
made significant progress over the last 5 years.
We have successfully demonstrated the feasibility
of a number of probabilistic models for relational
data, including probabilistic relational
models, Bayesian logic programs, and relational
probability trees, and the interest in SRL is
growing. However, in order to sustain and nurture
the growth of SRL as a subfield we need to
refocus our efforts on the science of machine
learning — moving from demonstrations to
comparative and ablation studies. We will outline
four assertions that are implicit to SRL research
but which have been only minimally
evaluated. We hope to stimulate discussion as to
how, as a community, these claims can be addressed
in future research.
@inproceedings{citeulike:430702,
abstract = {Statistical relational learning (SRL) research has
made significant progress over the last 5 years.
We have successfully demonstrated the feasibility
of a number of probabilistic models for relational
data, including probabilistic relational
models, Bayesian logic programs, and relational
probability trees, and the interest in SRL is
growing. However, in order to sustain and nurture
the growth of SRL as a subfield we need to
refocus our efforts on the science of machine
learning — moving from demonstrations to
comparative and ablation studies. We will outline
four assertions that are implicit to SRL research
but which have been only minimally
evaluated. We hope to stimulate discussion as to
how, as a community, these claims can be addressed
in future research.},
added-at = {2006-06-16T10:34:37.000+0200},
author = {Neville, Jennifer and Rattigan, Matthew and Jensen, David},
biburl = {https://www.bibsonomy.org/bibtex/2717bfe590cc10f3c2dc0d0d09655b411/ldietz},
booktitle = {Proceedings of the Workshop on Learning Statistical Models from Relational Data, 18th International Joint Conference on Artificial Intelligence.},
citeulike-article-id = {430702},
interhash = {6d36575c17f75bc69e1ea941cda5d8fb},
intrahash = {717bfe590cc10f3c2dc0d0d09655b411},
keywords = {relationalmodels},
priority = {2},
timestamp = {2006-06-16T10:34:37.000+0200},
title = {Statistical Relational Learning: Four Claims and a Survey},
url = {http://kdl.cs.umass.edu/papers/neville-et-al-srl2003.pdf},
year = 2003
}