Article

Dirichlet enhanced relational learning

Authors:

Zhao Xu,

Volker Tresp,

Kai Yu,

Shipeng Yu,

Hans-Peter KriegelAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 1004 - 1011

https://doi.org/10.1145/1102351.1102478

Published: 07 August 2005 Publication History

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Abstract

We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned by entities or relationships, and are coupled via a common prior distribution. Flexibility is added in a nonparametric hierarchical Bayesian approach, such that the learned knowledge can be truthfully represented. We apply our approach to a medical domain where we form a nonparametric hierarchical Bayesian model for relations involving hospitals, patients, procedures and diagnosis. The experiments show that the additional flexibility in a nonparametric hierarchical Bayes approach results in a more accurate model of the dependencies between procedures and diagnosis and gives significantly improved estimates of the probabilities of future procedures.

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Mutlu EOghaz TRajabi AGaribay I(2020)Review on Learning and Extracting Graph Features for Link PredictionMachine Learning and Knowledge Extraction10.3390/make20400362:4(672-704)Online publication date: 17-Dec-2020
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Xuan JLu JZhang G(2019)A Survey on Bayesian Nonparametric LearningACM Computing Surveys10.1145/329104452:1(1-36)Online publication date: 25-Jan-2019
https://dl.acm.org/doi/10.1145/3291044
Charnine MKlimenko S(2015)Measuring of "Idea-Based" Influence of Scientific PapersProceedings of the 2015 2nd International Conference on Information Science and Security (ICISS)10.1109/ICISSEC.2015.7371018(1-5)Online publication date: 14-Dec-2015
https://dl.acm.org/doi/10.1109/ICISSEC.2015.7371018
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Dirichlet enhanced relational learning
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Information & Contributors

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Published In

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Mutlu EOghaz TRajabi AGaribay I(2020)Review on Learning and Extracting Graph Features for Link PredictionMachine Learning and Knowledge Extraction10.3390/make20400362:4(672-704)Online publication date: 17-Dec-2020
https://doi.org/10.3390/make2040036
Xuan JLu JZhang G(2019)A Survey on Bayesian Nonparametric LearningACM Computing Surveys10.1145/329104452:1(1-36)Online publication date: 25-Jan-2019
https://dl.acm.org/doi/10.1145/3291044
Charnine MKlimenko S(2015)Measuring of "Idea-Based" Influence of Scientific PapersProceedings of the 2015 2nd International Conference on Information Science and Security (ICISS)10.1109/ICISSEC.2015.7371018(1-5)Online publication date: 14-Dec-2015
https://dl.acm.org/doi/10.1109/ICISSEC.2015.7371018
Jiang CWang JYu H(2013)Link Prediction Based on Sequential Bayesian Updating in a Terrorist NetworkFoundations and Applications of Intelligent Systems10.1007/978-3-642-37829-4_27(321-333)Online publication date: 23-Nov-2013
https://doi.org/10.1007/978-3-642-37829-4_27
Canh Hao Nguyen Mamitsuka H(2012)Latent Feature Kernels for Link Prediction on Sparse GraphsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2012.221533723:11(1793-1804)Online publication date: Nov-2012
https://doi.org/10.1109/TNNLS.2012.2215337
Bo YShuxia GNing LJun H(2011)Estimation with particle filter under model uncertainty2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)10.1109/ICSPCC.2011.6061671(1-5)Online publication date: Sep-2011
https://doi.org/10.1109/ICSPCC.2011.6061671
Lü LZhou T(2011)Link prediction in complex networks: A surveyPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2010.11.027390:6(1150-1170)Online publication date: Mar-2011
https://doi.org/10.1016/j.physa.2010.11.027
Hasan MZaki M(2011)A Survey of Link Prediction in Social NetworksSocial Network Data Analytics10.1007/978-1-4419-8462-3_9(243-275)Online publication date: 17-Mar-2011
https://doi.org/10.1007/978-1-4419-8462-3_9
Liu YNiculescu-Mizil AGryc WDanyluk ABottou LLittman M(2009)Topic-link LDAProceedings of the 26th Annual International Conference on Machine Learning10.1145/1553374.1553460(665-672)Online publication date: 14-Jun-2009
https://dl.acm.org/doi/10.1145/1553374.1553460
Kok SDomingos P(2007)Statistical predicate inventionProceedings of the 24th international conference on Machine learning10.1145/1273496.1273551(433-440)Online publication date: 20-Jun-2007
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