  Link prediction is the task of predicting missing connections between
entities in the knowledge graph (KG). While various forms of models are
proposed for the link prediction task, most of them are designed based on a few
known relation patterns in several well-known datasets. Due to the diversity
and complexity nature of the real-world KGs, it is inherently difficult to
design a model that fits all datasets well. To address this issue, previous
work has tried to use Automated Machine Learning (AutoML) to search for the
best model for a given dataset. However, their search space is limited only to
bilinear model families. In this paper, we propose a novel Neural Architecture
Search (NAS) framework for the link prediction task. First, the embeddings of
the input triplet are refined by the Representation Search Module. Then, the
prediction score is searched within the Score Function Search Module. This
framework entails a more general search space, which enables us to take
advantage of several mainstream model families, and thus it can potentially
achieve better performance. We relax the search space to be continuous so that
the architecture can be optimized efficiently using gradient-based search
strategies. Experimental results on several benchmark datasets demonstrate the
effectiveness of our method compared with several state-of-the-art approaches.
