# Markov logic network

## Papers overview

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Highly Cited

2010

Highly Cited

2010

- ICML
- 2010

Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners canâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- ICML
- 2009

Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these asâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- AAAI
- 2008

Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertaintyâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- ICML
- 2008

Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both firstâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- PKDD
- 2007

Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powerful and increasingly popularâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- ICML
- 2007

Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizesâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- Machine Learning
- 2006

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. Aâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- Sixth International Conference on Data Miningâ€¦
- 2006

Entity resolution is the problem of determining which records in a database refer to the same entities, and is a crucial andâ€¦Â (More)

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Highly Cited

2005

Highly Cited

2005

- AAAI
- 2005

Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNsâ€¦Â (More)

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Highly Cited

2005

Highly Cited

2005

- ICML
- 2005

Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these asâ€¦Â (More)

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