  In recent years, knowledge graphs have been widely applied to organize data
in a uniform way and enhance many tasks that require knowledge, for example,
online shopping which has greatly facilitated people's life. As a backbone for
online shopping platforms, we built a billion-scale e-commerce product
knowledge graph for various item knowledge services such as item
recommendation. However, such knowledge services usually include tedious data
selection and model design for knowledge infusion, which might bring
inappropriate results. Thus, to avoid this problem, we propose a Pre-trained
Knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge
graph, providing item knowledge services in a uniform way for embedding-based
models without accessing triple data in the knowledge graph. Notably, PKGM
could also complete knowledge graphs during servicing, thereby overcoming the
common incompleteness issue in knowledge graphs. We test PKGM in three
knowledge-related tasks including item classification, same item
identification, and recommendation. Experimental results show PKGM successfully
improves the performance of each task.
