This paper presents work in progress on an algorithm to track and identify changes in the vocabulary used to describe particular concepts over time, with emphasis on treating concepts as distinct from changes in word meaning. We apply the algorithm to word vectors generated from Google Books n-grams from 1800-1990 and evaluate the induced networks with respect to their flexibility (robustness to changes in vocabulary) and stability (they should not leap from topic to topic). We also describe work in progress using the British National Biography Linked Open Data Serials to construct a “ground truth” evaluation dataset for algorithms which aim to detect shifts in the vocabulary used to describe concepts. Finally, we discuss limitations of the proposed method, ways in which the method could be improved in the future, and other considerations.