Add to ORKGMarkov Logic can be used to induce inference rules from large knowledge bases, but it is hard to scale its algorithms to work on huge amounts of data. Thus, it is unfeasible to apply Markov Logic algorithms directly to a never-ending learning system, such as NELL (Never-Ending Language Learner). Still, it would be great if NELL could benefit from Markov Logic to extract useful inference rules from its knowledge base. Therefore, this paper aims to explore possible answers to the following question: How to scale Markov Logic to be used alongside NELL? 1
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
Markov logic uses weighted formulas to com-pactly encode a probability distribution over pos-sible w...
Markov Logic is a powerful representation that unifies first-order logic and probabilistic graphical...
Using Markov logic to integrate logical and distribu-tional information in natural-language semantic...
Markov logic networks (MLNs) combine the power of first-order logic and probabilistic graphical mode...
In this paper, we propose principled weight learning algorithms for Markov logic networks that can e...
In this paper, we propose principled weight learning algorithms for Markov logic networks that can e...
textMany real-world problems involve data that both have complex structures and uncertainty. Statist...
Abstract- Real-world data is most often presented in inconsistent, noisy, or incomplete state. Proba...
Abstract. Markov Logic is a powerful representation that unifies first-order logic and probabilistic...
Markov Logic Networks (MLNs) are weighted first-order logic templates for gen-erating large (ground)...
We examine how to scale up text-processing applications that are expressed in a language, Markov Log...
The Markov algorithm [1, 2] can be used as a language parser and as means fordefining languages [2–5...
Over the past two decades, statistical machine learning approaches to natural language processing ha...
Markov Logic Networks (MLNs) are weighted first-order logic templates for generating large (ground) ...
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
Markov logic uses weighted formulas to com-pactly encode a probability distribution over pos-sible w...
Markov Logic is a powerful representation that unifies first-order logic and probabilistic graphical...
Using Markov logic to integrate logical and distribu-tional information in natural-language semantic...
Markov logic networks (MLNs) combine the power of first-order logic and probabilistic graphical mode...
In this paper, we propose principled weight learning algorithms for Markov logic networks that can e...
In this paper, we propose principled weight learning algorithms for Markov logic networks that can e...
textMany real-world problems involve data that both have complex structures and uncertainty. Statist...
Abstract- Real-world data is most often presented in inconsistent, noisy, or incomplete state. Proba...
Abstract. Markov Logic is a powerful representation that unifies first-order logic and probabilistic...
Markov Logic Networks (MLNs) are weighted first-order logic templates for gen-erating large (ground)...
We examine how to scale up text-processing applications that are expressed in a language, Markov Log...
The Markov algorithm [1, 2] can be used as a language parser and as means fordefining languages [2–5...
Over the past two decades, statistical machine learning approaches to natural language processing ha...
Markov Logic Networks (MLNs) are weighted first-order logic templates for generating large (ground) ...
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combine...
Markov logic uses weighted formulas to com-pactly encode a probability distribution over pos-sible w...
Markov Logic is a powerful representation that unifies first-order logic and probabilistic graphical...