# Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs

**Authors:** Yao Zhang, Peiyao Li, Hongru Liang, Adam Jatowt, Zhenglu Yang  
**arXiv:** [2108.08297](https://arxiv.org/abs/2108.08297)  
**Date:** 2021-08-17  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2108.08297v2/

## Abstract

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.