Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base

TL;DR

This paper introduces a two-stage approach for formal query building in complex question answering over knowledge bases, combining query structure prediction with candidate ranking to improve accuracy.

Contribution

It proposes a novel graph generation framework for query structure prediction and integrates it with candidate ranking, enhancing performance on complex questions.

Findings

Outperforms existing methods on complex questions.

Maintains competitiveness on simple questions.

Introduces a new structure prediction framework.

Abstract

Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.