Modeling Context and the Characteristica Universalis

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This paper proposes prototypes for the exploration of the context of terms in a knowledge organization system by visualizing machine learning produced word embeddings. It puts this work in the context of the search for a universal language, typified by Leibniz’s characteristica universalis. This tradition of the search for universal languages is put in the context of universalizing tendencies in taxonomic classification in library and information science. Following this there is a discussion of the use of machine learning models to represent context. These two concerns inform the construction of prototypes for exploring the contextual spaces produced by word embeddings as a means to interrogating the act of classification in library and information science.