Markov logic networks
نویسندگان
چکیده
منابع مشابه
Hybrid Markov Logic Networks
Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most real-world applications also contain continuous ones. In this paper we introduce hybrid MLNs, in which continuous properties (e.g., the distan...
متن کاملEncoding Markov logic networks in Possibilistic Logic
Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a ...
متن کاملEfficient Weight Learning for Markov Logic Networks
Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powerful and increasingly popular representation for statistical relational learning. The state-of-the-art method for discriminative learning of MLN weights is the voted perceptron algorithm, which is essentially gradient descent with an MPE approximation to the expected sufficient statistics (true clause coun...
متن کاملMarkov Logic Networks with Numerical Constraints
Markov logic networks (MLNs) have proven to be useful tools for reasoning about uncertainty in complex knowledge bases. In this paper, we extend MLNs with numerical constraints and present an efficient implementation in terms of a cutting plane method. This extension is useful for reasoning over uncertain temporal data. To show the applicability of this extension, we enrich log-linear descripti...
متن کاملFocused Grounding for Markov Logic Networks
Markov logic networks have been successfully applied to many problems in AI. However, the computational complexity of the inference procedures has limited their application. Previous work in lifted inference, lazy inference and cutting plane inference has identified cases where the entire ground network need not be constructed. These approaches are specific to particular inference procedures, a...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 2006
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-006-5833-1