Reasoning with Probabilistic Logics

TL;DR

This paper introduces a probabilistic semantics for description logics, along with reasoning and learning algorithms, exemplified by the TRILL P system that computes query probabilities in probabilistic knowledge bases.

Contribution

It presents a novel probabilistic semantics for description logics and details the TRILL P system for probabilistic reasoning and learning, implemented in Prolog.

Findings

TRILL P computes probabilities of queries efficiently

The approach integrates probability with description logics effectively

The system is implemented and demonstrated in Prolog

Abstract

The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL P, which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been implemented in Prolog. Note: An extended abstract / full version of a paper accepted to be presented at the Doctoral Consortium of the 30th International Conference on Logic Programming (ICLP 2014), July 19-22, Vienna, Austria