Standpoint logic has been designed to support different standpoints on the same situation. It is proposed to compare this to the notion of context developed in artificial intelligence.
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”.
Logics of context (eg. [3,4]) have a tradition in artificial intelligence and many of them are closely related to Standpoint Logic. This research topic focuses in establishing results showing the differences in expressivity and complexity across some of the main formalisms in the field, and can be tailored to your specific areas of interest.
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)
[3] Serafini, L., Bouquet, P.: Comparing formal theories of context in AI. Artificial intelligence 155(1-2), 41–67 (2004)
[4] Klarman, S., Gutiérrez-Basulto, V.: Description logics of context. Journal of Logic and Computation 26(3), 817–854 (2013)
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Reference number: Proposal n°3072
Master profile: M2, research oriented.
Advisor: Lucía Gómez Álvarez (lucia:gomez_alvarez#tu-dresden:de) & Jérôme Euzenat (Jerome:Euzenat#inria:fr).
Team: The work will be carried out in the mOeX team common to INRIA & Université Grenoble Alpes. mOeX is dedicated to study knowledge evolution through adaptation. It gather permanent researchers from the Exmo team which has taken an active part these past 15 years in the development of the semantic web and more specifically ontology matching.
Laboratory: LIG.
Place of work: The position is located at INRIA Grenoble Rhône-Alpes, Montbonnot (near Grenoble, France) a main computer science research lab, in a stimulating research environment.
Perspectives: There is possibility to pursue in PhD.
Procedure: Contact us and provide vitæ and possibly motivation letter and references.