Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering
Year: 2019
Type: preprint
Abstract: Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results.
Source: arXiv (Cornell University)
Authors: Ding, Jiwei, Hu, Wei, Xu, Qixin, Qu, Yuzhong
Institution: Nanjing University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: 语义网的形式化查询的生成方法研究, 基于表示学习的RDF数据链接方法研究
OpenAlex ID: W2970409889