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Probabilistic Relational Models
Nir Friedman,
Lise Getoor,
Daphne Koller,
Uri Nodelman,
Avi Pfeffer,
Eran Segal,
Ben Taskar
Relational models are the most common representation of
structured data. Enterprise business information, marketing
and sales data, medical records, and scientific datasets are
all stored in relational databases. Recently, there has been
growing interest in extracting interesting statistical
patterns from these huge amounts of data. These patterns
give us a deeper understanding of our domain and the
relationships in it. Probabilistic Relational Models
(PRMs) are a language based on relational logic for
describing statistical models of structured data. In
addition to providing a sound and coherent foundation for
dealing with the noise and uncertainty encountered in most
real-world domains, the models themselves can be learned
directly from an existing database or knowledge base using
well-founded statistical techniques. PRMs model compex
domains in terms of entities, their properties, and the
relations between them. These models represent the
uncertainty over the properties of an entity, capturing its
probabilistic dependence both on other properties of that
entity and on properties of related entities. PRMs can even
represent uncertainty over the relational structure itself.
PRMs provide a new framework for relation data mining, and
offer new challenges for the endeavor of learning relational
models for real-world domains.
book chapter
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Learning Probabilistic Relational Models,
L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. Invited contribution to
the book Relational Data Mining, S. Dzeroski and N. Lavrac, Eds.,
Springer-Verlag, 2001 (to appear).
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refereed conferences
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Selectivity Estimation using Probabilistic Models L. Getoor, B. Taskar, and D. Koller,
To appear in Proceedings of the ACM SIGMOD International Conference on Management of Data, Santa Barbara, California, May 2001.
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SPOOK: A system for probabilistic object-oriented knowledge
representation A. Pfeffer, D. Koller, B. Milch, and K. Takusagawa,
In Proceedings of the 15th Annual Conference on Uncertainty in
AI (UAI), Stockholm, Sweden, August 1999, pages 541--550.
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Learning Probabilistic Relational Models,
N. Friedman, L. Getoor, D. Koller and A. Pfeffer.
Proceedings of the 16th International Joint Conference on Artificial
Intelligence (IJCAI), Stockholm, Sweden, August 1999, pages 1300--1307.
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Structured representation of complex
stochastic systems, N. Friedman, D. Koller, and A. Pfeffer.
Proceedings of the 15th National Conference on Artificial Intelligence
(AAAI), Madison, Wisconsin, July 1998, pages 157--164.
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Probabilistic frame-based systems,
D. Koller and A. Pfeffer. Proceedings of the 15th National Conference on
Artificial Intelligence (AAAI), Madison, Wisconsin, July 1998,
pages 580--587.
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Object-Oriented Bayesian Networks,
D. Koller and A. Pfeffer.
Proceedings of the 13th Annual Conference on Uncertainty in AI (UAI),
Providence, Rhode Island, August 1997, pages 302--313. Winner of the UAI
'97 best student paper award.
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DAGS - Daphne's Approximate Group of Students. All rights reserved.