---
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.\
    *To appear in Theory and Practice of Logic Programming (TPLP)*
author:
- |
    Angelika Kimmig, Bart Demoen and Luc De Raedt\
    Departement Computerwetenschappen, K.U. Leuven\
    Celestijnenlaan 200A - bus 2402, B-3001 Heverlee, Belgium\
    \
- |
    Vítor Santos Costa and Ricardo Rocha\
    CRACS $\&$ INESC-Porto LA, Faculty of Sciences, University of Porto\
    R. do Campo Alegre 1021/1055, 4169-007 Porto, Portugal\
    \
bibliography:
- 'tplp.bib'
title: On the Implementation of the Probabilistic Logic Programming Language ProbLog
---

\#1

Introduction {#sec:intro}
============

