  Scoring functions (SFs), which measure the plausibility of triplets in
knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which
target at capturing different kinds of relations in KGs, have been designed by
humans in recent years. However, as relations can exhibit complex patterns that
are hard to infer before training, none of them can consistently perform better
than others on existing benchmark data sets. In this paper, inspired by the
recent success of automated machine learning (AutoML), we propose to
automatically design SFs (AutoSF) for distinct KGs by the AutoML techniques.
However, it is non-trivial to explore domain-specific information here to make
AutoSF efficient and effective. We firstly identify a unified representation
over popularly used SFs, which helps to set up a search space for AutoSF. Then,
we propose a greedy algorithm to search in such a space efficiently. The
algorithm is further sped up by a filter and a predictor, which can avoid
repeatedly training SFs with same expressive ability and help removing bad
candidates during the search before model training. Finally, we perform
extensive experiments on benchmark data sets. Results on link prediction and
triplets classification show that the searched SFs by AutoSF, are KG dependent,
new to the literature, and outperform the state-of-the-art SFs designed by
humans.
