Knowledge Base Question Answering: A Semantic Parsing Perspective
Y. Gu, V. Pahuja, G. Cheng, and Y. Su. (2022)cite arxiv:2209.04994Comment: 19 pages, 3 figures; accepted to AKBC'22.
Abstract
Recent advances in deep learning have greatly propelled the research on
semantic parsing. Improvement 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 comparatively 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.
Description
Knowledge Base Question Answering: A Semantic Parsing Perspective
%0 Generic
%1 gu2022knowledge
%A Gu, Yu
%A Pahuja, Vardaan
%A Cheng, Gong
%A Su, Yu
%D 2022
%K llm machinelearning
%T Knowledge Base Question Answering: A Semantic Parsing Perspective
%U http://arxiv.org/abs/2209.04994
%X Recent advances in deep learning have greatly propelled the research on
semantic parsing. Improvement 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 comparatively 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.
@misc{gu2022knowledge,
abstract = {Recent advances in deep learning have greatly propelled the research on
semantic parsing. Improvement 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 comparatively 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.},
added-at = {2023-03-02T05:35:23.000+0100},
author = {Gu, Yu and Pahuja, Vardaan and Cheng, Gong and Su, Yu},
biburl = {https://www.bibsonomy.org/bibtex/24bdd86e7b9893fb91f123366d097ae62/sairahul},
description = {Knowledge Base Question Answering: A Semantic Parsing Perspective},
interhash = {6122a903c19f7b8fb53f068558341c99},
intrahash = {4bdd86e7b9893fb91f123366d097ae62},
keywords = {llm machinelearning},
note = {cite arxiv:2209.04994Comment: 19 pages, 3 figures; accepted to AKBC'22},
timestamp = {2023-03-02T05:35:23.000+0100},
title = {Knowledge Base Question Answering: A Semantic Parsing Perspective},
url = {http://arxiv.org/abs/2209.04994},
year = 2022
}