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Knowledge graphs are becoming ubiquitous in many scientific and industrial domains, ranging from biology, industrial engineering to natural language processing. In this work we explore how one of the largest currently available knowledge graphs, the Microsoft Concept Graph, can be used to construct interpretable features that are of potential use for the task of text classification. By exploiting graph-theoretic feature ranking, introduced as part of the existing tax2vec algorithm, we show thatdoi:10.5281/zenodo.4072960 fatcat:3mdwkippbfeinogkc6zm527l4i