AutoSF+: Towards Automatic Scoring Function Design for Knowledge Graph
  Embedding
  Scoring functions, which measure the plausibility of triples, have become the
crux of knowledge graph embedding (KGE). Plenty of scoring functions, targeting
at capturing different kinds of relations in KGs, have been designed by experts
in recent years. However, as relations can exhibit intricate patterns that are
hard to infer before training, none of them can consistently perform the best
on existing benchmark tasks. AutoSF has shown the significance of using
automated machine learning (AutoML) to design KG- dependent scoring functions.
In this paper, we propose AutoSF+ as an extension of AutoSF. First, we improve
the search algorithm with the evolutionary search, which can better explore the
search space. Second, we evaluate AutoSF+ on the recently developed benchmark
OGB. Besides, we apply AutoSF+ to the new task, i.e., entity classification, to
show that it can improve the task beyond KG completion.
