Relational Concept Analysis (RCA) is a multirelational data mining method that aims to extract knowledge from multiple formal contexts (i.e., objects, attributes, and a binary relation between them) and the relations between them. One of the problems RCA has is the lack of the possibility of extracting knowledge directly from data that is represented with ternary relations. While there are some existing solutions towards this problem, either they require complex preprocessing of the input data, or they lose some capabilities of RCA such as the different meanings of the relations between concepts (∃, ∀, etc). In this work, we present an intuitive extension to RCA to be able to use it with data directly represented with ternary relations. As an example of its usage, we apply it to a dataset called Knomana which includes ternary relationsPublicado en : ISOS Conference Proceedings Series.