  Knowledge graphs are essential for numerous downstream natural language
processing applications, but are typically incomplete with many facts missing.
This results in research efforts on multi-hop reasoning task, which can be
formulated as a search process and current models typically perform short
distance reasoning. However, the long-distance reasoning is also vital with the
ability to connect the superficially unrelated entities. To the best of our
knowledge, there lacks a general framework that approaches multi-hop reasoning
in mixed long-short distance reasoning scenarios. We argue that there are two
key issues for a general multi-hop reasoning model: i) where to go, and ii)
when to stop. Therefore, we propose a general model which resolves the issues
with three modules: 1) the local-global knowledge module to estimate the
possible paths, 2) the differentiated action dropout module to explore a
diverse set of paths, and 3) the adaptive stopping search module to avoid over
searching. The comprehensive results on three datasets demonstrate the
superiority of our model with significant improvements against baselines in
both short and long distance reasoning scenarios.
