  Linking entities like people, organizations, books, music groups and their
songs in text to knowledge bases (KBs) is a fundamental task for many
downstream search and mining applications. Achieving high disambiguation
accuracy crucially depends on a rich and holistic representation of the
entities in the KB. For popular entities, such a representation can be easily
mined from Wikipedia, and many current entity disambiguation and linking
methods make use of this fact. However, Wikipedia does not contain long-tail
entities that only few people are interested in, and also at times lags behind
until newly emerging entities are added. For such entities, mining a suitable
representation in a fully automated fashion is very difficult, resulting in
poor linking accuracy.
  What can automatically be mined, though, is a high-quality representation
given the context of a new entity occurring in any text. Due to the lack of
knowledge about the entity, no method can retrieve these occurrences
automatically with high precision, resulting in a chicken-egg problem. To
address this, our approach automatically generates candidate occurrences of
entities, prompting the user for feedback to decide if the occurrence refers to
the actual entity in question. This feedback gradually improves the knowledge
and allows our methods to provide better candidate suggestions to keep the user
engaged. We propose novel human-in-the-loop retrieval methods for generating
candidates based on gradient interleaving of diversification and textual
relevance approaches.
  We conducted extensive experiments on the FACC dataset, showing that our
approaches convincingly outperform carefully selected baselines in both
intrinsic and extrinsic measures while keeping users engaged.
