  Learning embeddings for entities and relations in knowledge graph (KG) have
benefited many downstream tasks. In recent years, scoring functions, the crux
of KG learning, have been human-designed to measure the plausibility of triples
and capture different kinds of relations in KGs. However, as relations exhibit
intricate patterns that are hard to infer before training, none of them
consistently perform the best on benchmark tasks. In this paper, inspired by
the recent success of automated machine learning (AutoML), we search bilinear
scoring functions for different KG tasks through the AutoML techniques.
However, it is non-trivial to explore domain-specific information here. We
first set up a search space for AutoBLM by analyzing existing scoring
functions. Then, we propose a progressive algorithm (AutoBLM) and an
evolutionary algorithm (AutoBLM+), which are further accelerated by filter and
predictor to deal with the domain-specific properties for KG learning. Finally,
we perform extensive experiments on benchmarks in KG completion, multi-hop
query, and entity classification tasks. Empirical results show that the
searched scoring functions are KG dependent, new to the literature, and
outperform the existing scoring functions. AutoBLM+ is better than AutoBLM as
the evolutionary algorithm can flexibly explore better structures in the same
budget.
