  We introduce and study knowledge drift (KD), a complex form of drift that
occurs in hierarchical classification. Under KD the vocabulary of concepts,
their individual distributions, and the is-a relations between them can all
change over time. The main challenge is that, since the ground-truth concept
hierarchy is unobserved, it is hard to tell apart different forms of KD. For
instance, introducing a new is-a relation between two concepts might be
confused with individual changes to those concepts, but it is far from
equivalent. Failure to identify the right kind of KD compromises the concept
hierarchy used by the classifier, leading to systematic prediction errors. Our
key observation is that in many human-in-the-loop applications (like smart
personal assistants) the user knows whether and what kind of drift occurred
recently. Motivated by this, we introduce TRCKD, a novel approach that combines
automated drift detection and adaptation with an interactive stage in which the
user is asked to disambiguate between different kinds of KD. In addition, TRCKD
implements a simple but effective knowledge-aware adaptation strategy. Our
simulations show that often a handful of queries to the user are enough to
substantially improve prediction performance on both synthetic and realistic
data.
