  Knowledge graphs (KGs) provide reliable external knowledge for a wide variety
of AI tasks in the form of structured triples. Knowledge graph pre-training
(KGP) aims to pre-train neural networks on large-scale KGs and provide unified
interfaces to enhance different downstream tasks, which is a key direction for
KG management, maintenance, and applications. Existing works often focus on
purely research questions in open domains, or they are not open source due to
data security and privacy in real scenarios. Meanwhile, existing studies have
not explored the training efficiency and transferability of KGP models in
depth. To address these problems, We propose a framework MuDoK to achieve
multi-domain collaborative pre-training and efficient prefix prompt tuning to
serve diverse downstream tasks like recommendation and text understanding. Our
design is a plug-and-play prompt learning approach that can be flexibly adapted
to different downstream task backbones. In response to the lack of open-source
benchmarks, we constructed a new multi-domain KGP benchmark called KPI with two
large-scale KGs and six different sub-domain tasks to evaluate our method and
open-sourced it for subsequent research. We evaluated our approach based on
constructed KPI benchmarks using diverse backbone models in heterogeneous
downstream tasks. The experimental results show that our framework brings
significant performance gains, along with its generality, efficiency, and
transferability.
