On the Implementation of the Probabilistic Logic Programming Language ProbLog

Abstract

The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.

28 pages; To appear in Theory and Practice of Logic Programming (TPLP)

References in corpus (1)

• Parameter Learning of Logic Programs for Symbolic-Statistical Modeling

Cited by in corpus (11)

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• kLog: A Language for Logical and Relational Learning with Kernels
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• Probabilistic QoS-aware Placement of VNF chains at the Edge
• Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach
• Explanations as Programs in Probabilistic Logic Programming
• Probabilistic Answer Set Programming with Discrete and Continuous Random Variables
• Generating Random Logic Programs Using Constraint Programming
• Integrating Belief Domains into Probabilistic Logic Programs
• Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue