Fuzzy spatial relation ontology for image interpretation

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Logic Tensor Networks for Semantic Image Interpretation (2017, methodology): “Other logic-based approaches have used fuzzy DL to tackle uncertainty in the basic components [Hudelot et al., 2008; Dasiopoulou et al., 2009; Atif et al., 2014].”
Integration of numeric and symbolic information for semantic image interpretation (2016, unknown): “[17]adopted afuzzyDLontologyofspatialrelationstodiscover objectsandspatialrelationsbetweentheminapic-turebycombiningbottom-upandtop-downmethods.”
Fuzzy Ontology Representation using OWL 2 (2010, unknown): “4 We will focus on just two applications of fuzzy ontologies: matchmaking problems and fuzzy multi-criteria decision making (MCDM) problems, but we would like to mention again that there are many more [12–15,17,18,26,28–32,45–49,56].”
Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, unknown): “In [50] the authors propose a fuzzy DL ontology of spatial relations and an algorithm for building scene graphs.”
Explanatory Reasoning for Image Understanding Using Formal Concept Analysis and Description Logics (2014, methodology): “To this aim, fuzzy representations of spatial entities and spatial relations in concrete domains have been proposed in our previous work [2] and later in [27] .”