Better Query Graph Selection for Knowledge Base Question Answering

TL;DR

This paper introduces a semantic parsing-based method for selecting optimal query graphs in KBQA, leveraging sequence modeling with BERT to improve ranking accuracy and achieve top results on benchmark datasets.

Contribution

It proposes a novel linearization and ranking approach using BERT for better query graph selection in KBQA, enhancing semantic interaction modeling.

Findings

Achieved top performance on ComplexQuestions dataset.

Secured second-best performance on WebQuestions.

Demonstrated improved ranking accuracy over previous methods.

Abstract

This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA). Specifically, we focus on how to select an optimal query graph from a candidate set so as to retrieve the answer from knowledge base (KB). In our approach, we first propose to linearize the query graph into a sequence, which is used to form a sequence pair with the question. It allows us to use mature sequence modeling, such as BERT, to encode the sequence pair. Then we use a ranking method to sort candidate query graphs. In contrast to the previous studies, our approach can efficiently model semantic interactions between the graph and the question as well as rank the candidate graphs from a global view. The experimental results show that our system achieves the top performance on ComplexQuestions and the second best performance on WebQuestions.