  Knowledge-graph-based reasoning has drawn a lot of attention due to its
interpretability. However, previous methods suffer from the incompleteness of
the knowledge graph, namely the interested link or entity that can be missing
in the knowledge graph(explicit missing). Also, most previous models assume the
distance between the target and source entity is short, which is not true on a
real-world KG like Freebase(implicit missing). The sensitivity to the
incompleteness of KG and the incapability to capture the long-distance link
between entities have limited the performance of these models on large KG. In
this paper, we propose a model that leverages the text corpus to cure such
limitations, either the explicit or implicit missing links. We model the
question answering on KG as a cooperative task between two agents, a knowledge
graph reasoning agent and an information extraction agent. Each agent learns
its skill to complete its own task, hopping on KG or select knowledge from the
corpus, via maximizing the reward for correctly answering the question. The
reasoning agent decides how to find an equivalent path for the given entity and
relation. The extraction agent provide shortcut for long-distance target entity
or provide missing relations for explicit missing links with messages from the
reasoning agent. Through such cooperative reward design, our model can augment
the incomplete KG strategically while not introduce much unnecessary noise that
could enlarge the search space and lower the performance.
