  Knowledge graphs (KGs), as structured representations of real world facts,
are intelligent databases incorporating human knowledge that can help machine
imitate the way of human problem solving. However, KGs are usually huge and
there are inevitably missing facts in KGs, thus undermining applications such
as question answering and recommender systems that are based on knowledge graph
reasoning. Link prediction for knowledge graphs is the task aiming to complete
missing facts by reasoning based on the existing knowledge. Two main streams of
research are widely studied: one learns low-dimensional embeddings for entities
and relations that can explore latent patterns, and the other gains good
interpretability by mining logical rules. Unfortunately, the heterogeneity of
modern KGs that involve entities and relations of various types is not well
considered in the previous studies. In this paper, we propose DegreEmbed, a
model that combines embedding-based learning and logic rule mining for
inferring on KGs. Specifically, we study the problem of predicting missing
links in heterogeneous KGs from the perspective of the degree of nodes.
Experimentally, we demonstrate that our DegreEmbed model outperforms the
state-of-the-art methods on real world datasets and the rules mined by our
model are of high quality and interpretability.
