Markov logic networks
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Generative MLNs learn a probabilistic logic program that explains all predicates in an interpretation, not a single target predicate.
“Generative MLNs learn a (probabilistic) logic program that explains all predicates in an interpretation, not a single target predicate.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Richardson & Domingos, 2006
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- Authors
- Richardson, M., & Domingos, P.
- Affiliations
- University of Washington (US)
- Corresponding author
- Domingos, Pedro
- Venue
- Machine Learning
- Published
- 2006-01-27
- Volume
- 62
- Issue
- 1-2
- Pages
- 107-136
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1977970897
- Cited by
- 2,676
- References
- 1
- Citations by year
- 2026: 32 · 2025: 52 · 2024: 70 · 2023: 82 · 2022: 85 · 2021: 127 · 2020: 163 · 2019: 169 · 2018: 154 · 2017: 156 · 2016: 193 · 2015: 191 · 2014: 180 · 2013: 184 · 2012: 175
- FWCI
- 122.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Funders
- Office of Naval Research (N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Computer Networks and Communications › Constraint Satisfaction and Optimization
- Keywords
- Markov logic networks, first-order logic, probabilistic graphical models, knowledge base, MCMC inference, inductive logic programming, relational databases, knowledge base learning, query answering
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