The importance of taking individual, potentially conflicting perspectives into account when dealing with knowledge has been widely recognised. Many existing ontology management approaches fully merge knowledge perspectives, which may require weakening in order to maintain consistency; others represent the distinct views in an entirely detached way. This talk presents an alternative, referred to as Standpoint Logic, a simple, yet versatile multi-modal logic “add-on” for existing KR languages intended for the integrated representation of domain knowledge relative to diverse standpoints, which can be hierarchically organised, combined, and put in relation with each other. 

Starting from the generic framework of First-Order Standpoint Logic (FOSL), we first present the fragment of so-called sentential formulas, for which we provide a polytime translation into the standpoint-free version. This result yields decidability and favourable complexities for several decidable fragments of first-order logic, including the very expressive description logic SROIQbs underlying the OWL 2 DL ontology language. By virtue of this, existing highly optimised OWL reasoners can be used to provide practical reasoning support for ontology languages extended by standpoint modelling.

Shifting our focus to tractable lightweight formalisms of enhanced scalability, we present Standpoint EL+, a standpoint extension of the popular description logic EL. Satisfiability in this logic can be checked in polynomial time thanks to a satisfiability-checking deduction calculus that allows for a implementation by means of elementary logic programming.

Sebastian Rudolph is full professor for computational logic and head of the AI Institute at TU Dresden. His scientific interests revolve around artificial intelligence, with a particular focus on logical formalisms and methods for knowledge representation and reasoning, ranging from theoretical foundations (such as semantic and computational properties) to practical deployment (including ontological modeling and interactive knowledge acquisition). In the course of an ongoing ERC Consolidator Grant, his team currently investigates general principles of decidability in logic-based knowledge representation.