Knowledge base question answering (KBQA) is a challenging task that aims to
retrieve correct answers from large-scale knowledge bases. Existing attempts
primarily focus on entity representation and final answer reasoning, which
results in limited supervision for this task. Moreover, the relations, which
empirically determine the reasoning path selection, are not fully considered in
recent advancements. In this study, we propose a novel framework, RE-KBQA, that
utilizes relations in the knowledge base to enhance entity representation and
introduce additional supervision. We explore guidance from relations in three
aspects, including (1) distinguishing similar entities by employing a
variational graph auto-encoder to learn relation importance; (2) exploring
extra supervision by predicting relation distributions as soft labels with a
multi-task scheme; (3) designing a relation-guided re-ranking algorithm for
post-processing. Experimental results on two benchmark datasets demonstrate the
effectiveness and superiority of our framework, improving the F1 score by 5.7%
from 40.5 to 46.3 on CWQ and 5.8% from 62.8 to 68.5 on WebQSP, better or on par
with state-of-the-art methods.

本文提出了一种新的 RE-KBQA 框架，利用知识库中的关系来增强实体表示并引入额外的监督方法以提高知识库问答的效果，通过实验证明此框架的优越性，从而提高 F1 分数。