# Better Query Graph Selection for Knowledge Base Question Answering

Source: [Better Query Graph Selection for Knowledge Base Question Answering](<https://www.sota2.com/research/paper/68fa6c691e209131cea33ef5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:10:48.263Z

Authors: Yonghui Jia, Wenliang Chen

Published: 2022-04-27T01:53:06.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2204.12662>)

[PDF](<https://arxiv.org/pdf/2204.12662.pdf>)