Standpoint logic has been designed to support different standpoints on the same situation. Extending it with support for uncertain events will provide it with a dynamic aspect.
Ontologies and knowledge bases can be used to conceptualise specific domains. These knowledge sources reflect the points of view of their creators, as well as other contextual aspects and modelling design decisions. This semantic heterogeneity will often lead to inconsistencies and unintended inferences when we try to make sources interoperable.
Standpoint Logic [1,2] is a multi-modal logic intended for the integrated representation of knowledge relative to diverse, possibly conflicting standpoints or perspectives. It addresses this challenge by supporting the coexistence of multiple standpoints and the establishment of alignments between them. This is particularly important in scenarios that require the simultaneous consideration of multiple viewpoints. To do this, we extend a given base language (for instance a description logic or propositional logic) with labelled modal operators, such that propositions []LC φ and <>LC φ express information relative to the standpoint LC and read, respectively: “according to LC, it is unequivocal/conceivable that φ”.
This research topic involves exploring and extending Standpoint Logic to handle dynamic or probabilistic reasoning. Dynamic logics allow us to model how knowledge and information change when events occur. Probabilistic reasoning allows for quantifying uncertainty in the represented knowledge. Research in this area would focus on developing formal frameworks for integrating dynamic and probabilistic reasoning with Standpoint Logic.
References:
[1] Gómez Álvarez, L., Rudolph, S.: Standpoint logic: Multi-perspective knowledge representation. In: Neuhaus, F., Brodaric, B. (eds.) Procs. of the 12th Int. Conf. on Formal Ontology in Information Systems (FOIS). FAIA, vol. 344, pp. 3–17. IOS Press (2021)
[2] Gómez Álvarez, L., Rudolph, S., Strass, H.: How to Agree to Disagree: Managing Ontological Perspectives using Standpoint Logic. In: Sattler, U., Hogan, A., Keet, C.M., Presutti, V., Almeida, J.P.A., Takeda, H., Monnin, P., Pirrò, G., d’Amato, C. (eds.) Proceedings of the 21st International Semantic Web Conference (ISWC). pp. 125–141. Springer (2022)
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