A Framework for Reasoning on Probabilistic Description Logics

TL;DR

This paper presents BUNDLE, a flexible inference framework for Probabilistic Description Logics that integrates with existing OWL reasoners, enabling probabilistic reasoning and performance comparison across various settings.

Contribution

It introduces BUNDLE's new interface with TRILL reasoners, enhancing probabilistic reasoning capabilities and providing a versatile tool for diverse applications.

Findings

BUNDLE can interface with TRILL reasoners for probabilistic inference.

Performance varies depending on reasoner and method used.

The framework is adaptable as a standalone app or library.

Abstract

While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.