On the Implementation of the Probabilistic Logic Programming Language ProbLog

TL;DR

This paper presents ProbLog, a probabilistic extension of Prolog designed for efficient querying in large biological networks, combining probabilistic reasoning with logic programming.

Contribution

It introduces algorithms for probabilistic query execution in ProbLog and discusses their implementation and performance in biological network analysis.

Findings

Efficient algorithms enable scalable probabilistic querying.

Implementation on YAP-Prolog demonstrates practical applicability.

Performance evaluation shows suitability for large biological networks.

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.