Formal Concept Analysis (FCA) comes with a range of rel- evant techniques for knowledge analysis, such as conceptual structures or implications. The Duquenne-Guigues basis of implications provides a cardinality minimal set of non-redundant implications. The concern of a domain expert is to discover new knowledge within this implication set. The objective of this paper is to collect and discuss the di_erent pat- terns of implications extracted from a dataset on plants used in medical care or consumed as food. We identify 16 patterns combining 3 types of knowledge elements (KE). The patterns highlight redundant KEs, in particular, those corresponding to plant taxonomy, as it is familiar knowl- edge for the experts. Removing these KEs from the implications makes them tacit. We suggest a post-process for cleaning up the implications before reporting them to the experts.