Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link
  Prediction in Knowledge Graphs
Nicolas Hubert Pierre Monnin Heiko Paulheim
Knowledge graphs (KGs) comprise entities interconnected by relations of
different semantic meanings. KGs are being used in a wide range of
applications. However, they inherently suffer from incompleteness, i.e.
entities or facts about entities are missing. Consequently, a larger body of
works focuses on the completion of missing information in KGs, which is
commonly referred to as link prediction (LP). This task has traditionally and
extensively been studied in the transductive setting, where all entities and
relations in the testing set are observed during training. Recently, several
works have tackled the LP task under more challenging settings, where entities
and relations in the test set may be unobserved during training, or appear in
only a few facts. These works are known as inductive, few-shot, and zero-shot
link prediction. In this work, we conduct a systematic review of existing works
in this area. A thorough analysis leads us to point out the undesirable
existence of diverging terminologies and task definitions for the
aforementioned settings, which further limits the possibility of comparison
between recent works. We consequently aim at dissecting each setting
thoroughly, attempting to reveal its intrinsic characteristics. A unifying
nomenclature is ultimately proposed to refer to each of them in a simple and
consistent manner.