  The problem of knowledge graph (KG) reasoning has been widely explored by
traditional rule-based systems and more recently by knowledge graph embedding
methods. While logical rules can capture deterministic behavior in a KG they
are brittle and mining ones that infer facts beyond the known KG is
challenging. Probabilistic embedding methods are effective in capturing global
soft statistical tendencies and reasoning with them is computationally
efficient. While embedding representations learned from rich training data are
expressive, incompleteness and sparsity in real-world KGs can impact their
effectiveness. We aim to leverage the complementary properties of both methods
to develop a hybrid model that learns both high-quality rules and embeddings
simultaneously. Our method uses a cross feedback paradigm wherein, an embedding
model is used to guide the search of a rule mining system to mine rules and
infer new facts. These new facts are sampled and further used to refine the
embedding model. Experiments on multiple benchmark datasets show the
effectiveness of our method over other competitive standalone and hybrid
baselines. We also show its efficacy in a sparse KG setting and finally explore
the connection with negative sampling.
