Web Table Extraction, Retrieval and Augmentation: A Survey
arXiv:2002.00207
Abstract
Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represents a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.
ACM Transactions on Intelligent Systems and Technology. 11(2): Article 13, January 2020
References in corpus (7)
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Cited by in corpus (6)
- Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
- Correlation Sketches for Approximate Join-Correlation Queries
- GitTables: A Large-Scale Corpus of Relational Tables
- TCN: Table Convolutional Network for Web Table Interpretation
- Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System
- Information Extraction From Co-Occurring Similar Entities