Table understanding: Problem overview

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2022-11-21 DOI:10.1002/widm.1482

A. Shigarov

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引用次数: 5

Abstract

Tables are probably the most natural way to represent relational data in various media and formats. They store a large number of valuable facts that could be utilized for question answering, knowledge base population, natural language generation, and other applications. However, many tables are not accompanied by semantics for the automatic interpretation of the information they present. Table Understanding (TU) aims at recovering the missing semantics that enables the extraction of facts from tables. This problem covers a range of issues from table detection in document images to semantic table interpretation with the help of external knowledge bases. To date, the TU research has been ongoing on for 30 years. Nevertheless, there is no common point of view on the scope of TU; the terminology still needs agreement and unification. In recent years, science and technology have shown a rapidly increasing interest in TU. Nowadays, it is especially important to check the meaning of this research problem once again. This article gives a comprehensive characterization of the TU problem, including a description of its subproblems, tasks, subtasks, and applications. It also discusses the common limitations used in the existing problem statements and proposes some directions for further research that would help overcome the corresponding limitations.

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表理解:问题概述

表可能是用各种媒体和格式表示关系数据的最自然的方式。它们存储了大量有价值的事实，可用于问题回答、知识库填充、自然语言生成和其他应用程序。然而，许多表没有语义来自动解释它们所表示的信息。表理解(Table Understanding, TU)旨在恢复丢失的语义，从而能够从表中提取事实。这个问题涵盖了从文档图像中的表检测到借助外部知识库进行语义表解释的一系列问题。迄今为止，TU的研究已经进行了30年。然而，对于TU的范围并没有统一的观点;术语仍然需要一致和统一。近年来，科学技术对TU的兴趣迅速增加，在今天，重新审视这一研究问题的意义显得尤为重要。本文对TU问题进行了全面的描述，包括对其子问题、任务、子任务和应用程序的描述。它还讨论了现有问题陈述中使用的常见限制，并提出了一些有助于克服相应限制的进一步研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.