Table extraction

Table extraction is the process of recognizing and separating a table from a large document, possibly also recognizing individual rows, columns or elements. It may be regarded as a special form of information extraction.

Table extractions from webpages can take advantage of the special HTML elements that exist for tables, e.g., the "table" tag, and programming libraries may implement table extraction from webpages. The Python pandas software library can extract tables from HTML webpages via its read_html() function.

More challenging is table extraction from PDFs or scanned images, where there usually is no table-specific machine readable markup.[1] Systems that extract data from tables in scientific PDFs have been described.[2][3]

Wikipedia presents some of its information in tables, and, e.g., 3.5 million tables can be extracted from the English Wikipedia.[4] Some of the tables have a specific format, e.g., the so-called infoboxes. Large-scale table extraction of Wikipedia infoboxes forms one of the sources for DBpedia.[5]

Commercial web services for table extraction exist, e.g., Amazon Textract, Google's Document AI, IBM Watson Discovery, and Microsoft Form Recognizer.[1] Open source tools also exist, e.g., PDFFigures 2.0 that has been used in Semantic Scholar.[6] In a comparison published in 2017, the researchers found the proprietary program ABBYY FineReader to yield the best PDF table extraction performance among six different tools evaluated.[7]

References
  1. Douglas Burdick; Marina Danilevsky; Alexandre V Evfimievski; Yannis Katsis; Nancy Wang (August 2020). "Table extraction and understanding for scientific and enterprise applications". Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases. 13 (12): 3433–3436. doi:10.14778/3415478.3415563. ISSN 2150-8097. Wikidata Q108170445.
  2. Wenhao Yu; Wei Peng; Yu Shu; Qingkai Zeng; Meng Jiang (19 April 2020). Experimental Evidence Extraction System in Data Science with Hybrid Table Features and Ensemble Learning. pp. 951–961. doi:10.1145/3366423.3380174. ISBN 978-1-4503-7023-3. Wikidata Q108172460. {{cite book}}: |journal= ignored (help)
  3. Benno Kruit; Hongyu He; Jacopo Urbani (1 November 2020). Tab2Know: Building a Knowledge Base from Tables in Scientific Papers. pp. 349–365. arXiv:2107.13306. doi:10.1007/978-3-030-62419-4_20. ISBN 978-3-030-62419-4. Wikidata Q101086651. {{cite book}}: |journal= ignored (help)
  4. Tobias Bleifuß; Leon Bornemann; Dmitri V. Kalashnikov; Felix Naumann; Divesh Srivastava (17 August 2021). "The Secret Life of Wikipedia Tables" (PDF). Proceedings of the 2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores. CEUR Workshop Proceedings: 20–26. Wikidata Q108215401.
  5. Sören Auer; Christian Bizer; Georgi Kobilarov; Jens Lehmann; Richard Cyganiak; Zachary Ives (2007). DBpedia: A Nucleus for a Web of Open Data. pp. 722–735. doi:10.1007/978-3-540-76298-0_52. ISBN 978-3-540-76297-3. Wikidata Q27910422. {{cite book}}: |journal= ignored (help)
  6. Christopher Clark; Santosh Divvala (2016). PDFFigures 2.0: Mining figures from research papers. ISBN 978-1-4503-4229-2. Wikidata Q108172042. {{cite book}}: |journal= ignored (help)
  7. Andreiwid Sheffer Corrêa; Pär-Ola Zander (7 June 2017), Unleashing Tabular Content to Open Data: A Survey on PDF Table Extraction Methods and Tools, doi:10.1145/3085228.3085278, Wikidata Q108173686
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