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Highly Cited

2011

Highly Cited

2011

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been… Expand

Highly Cited

2009

Highly Cited

2009

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this… Expand

Highly Cited

2008

Highly Cited

2008

Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty… Expand

Highly Cited

2008

Highly Cited

2008

We address the problem of visual event recognition in surveillance where noise and missing observations are serious problems… Expand

Highly Cited

2008

Highly Cited

2008

Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both first… Expand

Highly Cited

2007

Highly Cited

2007

Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powerful and increasingly popular… Expand

Highly Cited

2006

Highly Cited

2006

We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A… Expand

Highly Cited

2006

Highly Cited

2006

Entity resolution is the problem of determining which records in a database refer to the same entities, and is a crucial and… Expand

Highly Cited

2005

Highly Cited

2005

Many machine learning applications require a combination of probability and first-order logic. Markov logic networks (MLNs… Expand

Highly Cited

2005

Highly Cited

2005

Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as… Expand