Petra Murinová, Michal Burda: Linguistic Characterization of Natural Data by Applying Intermediate Quantifiers on Fuzzy Association Rules The objective of this presentation is to introduce fuzzy natural logic together with the Fuzzy GUHA method for analysis and linguistic characterization of scientific data. Fuzzy GUHA is a tool for extracting linguistic association rules from data. Obtained associations are IF-THEN rules composed of evaluative linguistic expressions, which allow the quantities to be characterized with vague linguistic terms such as "very small", "big", "medium" etc. Originally, fuzzy GUHA provides several numerical indices of rule quality, which may not be easily understandable for domain experts that are not familiar with GUHA association rules. Therefore, we will present the main theories of fuzzy natural logic, mainly the theory of intermediate quantifiers and the theory of syllogistic reasoning and graded square of opposition. We will show how these theories can be applied to the results in an automatic manner in order to obtain natural linguistic summarization.