  In this paper, we propose a new product knowledge graph (PKG) embedding
approach for learning the intrinsic product relations as product knowledge for
e-commerce. We define the key entities and summarize the pivotal product
relations that are critical for general e-commerce applications including
marketing, advertisement, search ranking and recommendation. We first provide a
comprehensive comparison between PKG and ordinary knowledge graph (KG) and then
illustrate why KG embedding methods are not suitable for PKG learning. We
construct a self-attention-enhanced distributed representation learning model
for learning PKG embeddings from raw customer activity data in an end-to-end
fashion. We design an effective multi-task learning schema to fully leverage
the multi-modal e-commerce data. The Poincare embedding is also employed to
handle complex entity structures. We use a real-world dataset from
grocery.walmart.com to evaluate the performances on knowledge completion,
search ranking and recommendation. The proposed approach compares favourably to
baselines in knowledge completion and downstream tasks.
