  The task of Knowledge Graph Completion (KGC) aims to automatically infer the
missing fact information in Knowledge Graph (KG). In this paper, we take a new
perspective that aims to leverage rich user-item interaction data (user
interaction data for short) for improving the KGC task. Our work is inspired by
the observation that many KG entities correspond to online items in application
systems. However, the two kinds of data sources have very different intrinsic
characteristics, and it is likely to hurt the original performance using simple
fusion strategy. To address this challenge, we propose a novel adversarial
learning approach by leveraging user interaction data for the KGC task. Our
generator is isolated from user interaction data, and serves to improve the
performance of the discriminator. The discriminator takes the learned useful
information from user interaction data as input, and gradually enhances the
evaluation capacity in order to identify the fake samples generated by the
generator. To discover implicit entity preference of users, we design an
elaborate collaborative learning algorithms based on graph neural networks,
which will be jointly optimized with the discriminator. Such an approach is
effective to alleviate the issues about data heterogeneity and semantic
complexity for the KGC task. Extensive experiments on three real-world datasets
have demonstrated the effectiveness of our approach on the KGC task.
