  In recent years, knowledge graph embedding becomes a pretty hot research
topic of artificial intelligence and plays increasingly vital roles in various
downstream applications, such as recommendation and question answering.
However, existing methods for knowledge graph embedding can not make a proper
trade-off between the model complexity and the model expressiveness, which
makes them still far from satisfactory. To mitigate this problem, we propose a
lightweight modeling framework that can achieve highly competitive relational
expressiveness without increasing the model complexity. Our framework focuses
on the design of scoring functions and highlights two critical characteristics:
1) facilitating sufficient feature interactions; 2) preserving both symmetry
and antisymmetry properties of relations. It is noteworthy that owing to the
general and elegant design of scoring functions, our framework can incorporate
many famous existing methods as special cases. Moreover, extensive experiments
on public benchmarks demonstrate the efficiency and effectiveness of our
framework. Source codes and data can be found at
\url{https://github.com/Wentao-Xu/SEEK}.
