  In recent years, knowledge graphs have been widely applied as a uniform way
to organize data and have enhanced many tasks requiring knowledge. In online
shopping platform Taobao, we built a billion-scale e-commerce product knowledge
graph. It organizes data uniformly and provides item knowledge services for
various tasks such as item recommendation. Usually, such knowledge services are
provided through triple data, while this implementation includes (1) tedious
data selection works on product knowledge graph and (2) task model designing
works to infuse those triples knowledge. More importantly, product knowledge
graph is far from complete, resulting error propagation to knowledge enhanced
tasks. To avoid these problems, we propose a Pre-trained Knowledge Graph Model
(PKGM) for the billion-scale product knowledge graph. On the one hand, it could
provide item knowledge services in a uniform way with service vectors for
embedding-based and item-knowledge-related task models without accessing triple
data. On the other hand, it's service is provided based on implicitly completed
product knowledge graph, overcoming the common the incomplete issue. We also
propose two general ways to integrate the service vectors from PKGM into
downstream task models. We test PKGM in five knowledge-related tasks, item
classification, item resolution, item recommendation, scene detection and
sequential recommendation. Experimental results show that PKGM introduces
significant performance gains on these tasks, illustrating the useful of
service vectors from PKGM.
