Open and Closed World Assumptions in Data Exchange
OWL Reasoning in the Real World: Searching for Godot
I will provide an overview of many of the use cases that we looked at to apply OWL ABox reasoning in the real world. The fields we covered included (a) healthcare, and life sciences where the plethora of ontologies might be seen as providing a strong use case for OWL reasoning, (b) information retrieval over unstructured text, (c) master data management, which involves reasoning over product and consumer data incorporated from multiple data sources within an enterprise. In each case, we ran into a series of bottlenecks including incorrect modeling of constructs in the ontology, inherent difficulties in scaling OWL reasoning to real world requirements, needing formalisms outside that of OWL, and needing techniques to semi-automate the construction of ontologies. At least in the use cases we had seen, there is a need for performing TBox OWL reasoning over expressive ontologies, but most realistic uses of ABox reasoning have to be relatively simple in terms of expressivity, for practical reasons.
Global Caching, Inverse Roles and Fixpoint Logics
I will begin by explaining an optimal tableau-based algorithm for checking ALC-satisfiability which uses "global caching" and which appears to work well in practice. The algorithm settles a conjecture that "global caching can lead to optimality". I will then explain how "global caching" can be extended to "global state caching" for inverse roles, thereby extending the result to ALCI-satisfiability, and converse roles in general. Finally, I will explain how "global caching" can be used to give optimal "on the fly" tableau decision procedures for some fixpoint logics. Finally, some open questions. The talk is intended to be expository so it will be at a fairly high level.