Yu Gu, Vardaan Pahuja, Gong Cheng, Yu Su.

Recent advances in deep learning have greatly propelled the research on semantic parsing. Im- provement has since been made in many downstream tasks, including natural language interface to web APIs, text-to-SQL generation, among others. However, despite the close connection shared with these tasks, research on question answering over knowledge bases (KBQA) has compara- tively been progressing slowly. We identify and attribute this to two unique challenges of KBQA, schema-level complexity and fact-level complexity. In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges. Regardless of the unique challenges, we argue that we can still take much inspiration from the literature of semantic parsing, which has been overlooked by existing research on KBQA. Based on our discussion, we can better understand the bottleneck of current KBQA research and shed light on promising directions for KBQA to keep up with the literature of semantic parsing, particularly in the era of pre-trained language models.

Citation

@inproceedings{
gu2022knowledge,
title={Knowledge Base Question Answering: A Semantic Parsing Perspective},
author={Yu Gu and Vardaan Pahuja and Gong Cheng and Yu Su},
booktitle={4th Conference on Automated Knowledge Base Construction},
year={2022}
}