A Framework for Reasoning on Probabilistic Description Logics
This work addresses the problem of reasoning under uncertainty in knowledge representation for AI researchers, but it is incremental as it builds on existing frameworks.
The authors tackled the lack of reasoning tools for uncertainty in Description Logics by advancing the BUNDLE framework, which now interfaces with TRILL system reasoners to provide a uniform method for probabilistic queries, with performance varying based on the reasoner and method used.
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.