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Towards digesting the alphabet-soup of statistical relational learning
TLDR
This paper reports on our work towards the development of a probabilistic logic programming environment intended as a target language in which other Probabilistic languages can be compiled, thereby contributing to the digestion of the “alphabet soup”. Expand
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Dedicated Tabling for a Probabilistic Setting
ProbLog is a probabilistic framework that extends Prolog with probabilistic facts. To compute the probability of a query, the complete SLD proof tree of the query is collected as a sum of products.Expand
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ProbLog Technology for Inference in a Probabilistic First Order Logic
TLDR
We introduce First Order ProbLog, an extension of first order logic with soft constraints where formulas are guarded by probabilistic facts. Expand
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Tabling, Rational Terms, and Coinduction Finally Together!
TLDR
We report our extension of YAP's Prolog tabling mechanism to support rational terms. Expand
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Adaptive Sojourn Time HSMM for Heart Sound Segmentation
TLDR
We propose a new algorithm for heart sound segmentation using a hidden semi-Markov model. Expand
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Analyzing the efficiency of context-based grouping on collaboration in VANETs with large-scale simulation
TLDR
We propose a context-based routing scheme for vehicle-to-vehicle communication based on contextual properties such as location, direction, and information interest to reduce the network traffic overhead. Expand
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Nesting Probabilistic Inference
TLDR
We propose the notion of a parametrised ProbLog engine to support nested probabilistic inference in ProbLog. Expand
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Tabling relevant parts of SLD proofs for ground goals in a probabilistic setting
TLDR
We investigate how the idea of tabling can be used in ProbLog: the challenge is that we are not just interested in the answers of a query but in retaining the SLD proof tree. Expand
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Probabilistic Argumentation Frameworks with MetaProbLog and ConArg
TLDR
In Probabilistic Abstract Argumentation, arguments and attacks (nodes and edges) in a graph instance are associated with a probability value. Expand
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Preprocessing Boolean Formulae for BDDs in a Probabilistic Context
TLDR
Inference in many probabilistic logic systems is based on representing the proofs of a query as a DNF Boolean formula. Expand
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