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Measurement of the neutrino velocity with the OPERA detector in the CNGS beam
A bstractThe OPERA neutrino experiment at the underground Gran Sasso Laboratory has measured the velocity of neutrinos from the CERN CNGS beam over a baseline of about 730 km. The measurement isExpand
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Reasoning with Probabilistic Ontologies
TLDR
We present the algorithm BUNDLE for computing the probability of queries from probabilistic ontologies following the DISPONTE semantics. Expand
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Probabilistic Description Logics under the distribution semantics
TLDR
We present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases that is based on the distribution semantics for probabilistic logic programs. Expand
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Structure learning of probabilistic logic programs by searching the clause space
Abstract Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) orExpand
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The PITA system: Tabling and answer subsumption for reasoning under uncertainty
TLDR
In this paper, we show how the PITA system, which originally supported the general PLP language of LPADs, can also efficiently support restricted PLP and Possibilistic Logic Programs. Expand
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Artificial intelligence techniques for monitoring dangerous infections
TLDR
The monitoring and detection of nosocomial infections is a very important problem arising in hospitals. Expand
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SWISH: SWI-Prolog for Sharing
TLDR
This article describes SWISH, a web front-end for Prolog, which is used to run small Prolog programs for demonstration, experimentation and education. Expand
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Exploiting Inductive Logic Programming Techniques for Declarative Process Mining
TLDR
In this paper, we present a logic-based approach for tackling this problem. Expand
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Applying Inductive Logic Programming to Process Mining
TLDR
In this paper we propose a language for the representation of process models that is inspired to the SCIFF language and is an extension of clausal logic. Expand
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Expectation maximization over binary decision diagrams for probabilistic logic programs
TLDR
We present a Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools. Expand
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