We introduce Qiana, a logic framework for reasoning on formulas that are true only in specific contexts. In Qiana, it is possible to quantify over both formulas and contexts to express, e.g., that “everyone knows everything Alice says”. Qiana also permits paraconsistent logics within contexts, so that contexts can contain contradictions. Furthermore, Qiana is based on first-order logic, and is finitely axiomatizable, so that Qiana theories are compatible with pre-existing first-order logic theorem provers. We show how Qiana can be used to represent temporality, event calculus, and modal logic. We also discuss different design alternatives of Qiana.

We introduce Qiana, a logic framework for reasoning on formulas that are true only in specific contexts. In Qiana, it is possible to quantify over both formulas and contexts to express, e.g., that ``everyone knows everything Alice says''. Qiana also permits paraconsistent logics within contexts, so that contexts can contain contradictions. Furthermore, Qiana is based on first-order logic, and is finitely axiomatizable, so that Qiana theories are compatible with pre-existing first-order logic theorem provers.

Chadi Helwe, Tom Calamai, Pierre-Henri Paris, Chloé Clavel, Fabian Suchanek. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

International audience

With an increase in the number of Linked Open Data datasets, insufficient interlinking quality can lead to a decrease in overall data quality. Therefore, it is necessary to keep the interlinking quality as high as possible. One of the main ways to link datasets is to use owl:sameAs links, i.e. to indicate that two things are the same. But with its strict semantics, there is a lot of misuse of owl:sameAs in the wild. Indeed, identity is often relative and depends on the context of use. We therefore propose an approach that enables considering the characteristics of involved datasets to interlink datasets thanks to owl:sameAs statements. The experimental results performed on real-world datasets show that the proposed approach is promising.