  In the question answering(QA) task, multi-hop reasoning framework has been
extensively studied in recent years to perform more efficient and interpretable
answer reasoning on the Knowledge Graph(KG). However, multi-hop reasoning is
inapplicable for answering n-ary fact questions due to its linear reasoning
nature. We discover that there are two feasible improvements: 1) upgrade the
basic reasoning unit from entity or relation to fact; and 2) upgrade the
reasoning structure from chain to tree. Based on these, we propose a novel
fact-tree reasoning framework, through transforming the question into a fact
tree and performing iterative fact reasoning on it to predict the correct
answer. Through a comprehensive evaluation on the n-ary fact KGQA dataset
introduced by this work, we demonstrate that the proposed fact-tree reasoning
framework has the desired advantage of high answer prediction accuracy. In
addition, we also evaluate the fact-tree reasoning framework on two binary KGQA
datasets and show that our approach also has a strong reasoning ability
compared with several excellent baselines. This work has direct implications
for exploring complex reasoning scenarios and provides a preliminary baseline
approach.
