A Method of Query Graph Reranking for Knowledge Base Question Answering

TL;DR

This paper introduces a two-step reranking approach for selecting the best query graph in KBQA, significantly improving top-1 accuracy by leveraging answer type information.

Contribution

It proposes a novel query graph reranking method that reduces the gap between top-1 performance and oracle scores in KBQA.

Findings

Achieves best results on WebQuestions dataset.

Second best performance on ComplexQuestions dataset.

Effectively narrows the top-1 and oracle score gap.

Abstract

This paper presents a novel reranking method to better choose the optimal query graph, a sub-graph of knowledge graph, to retrieve the answer for an input question in Knowledge Base Question Answering (KBQA). Existing methods suffer from a severe problem that there is a significant gap between top-1 performance and the oracle score of top-n results. To address this problem, our method divides the choosing procedure into two steps: query graph ranking and query graph reranking. In the first step, we provide top-n query graphs for each question. Then we propose to rerank the top-n query graphs by combining with the information of answer type. Experimental results on two widely used datasets show that our proposed method achieves the best results on the WebQuestions dataset and the second best on the ComplexQuestions dataset.