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] In a 2023 benchmark evaluation,[8] Adobe Extract,[9] a cloud-based API that employs Adobe’s Sensei AI-platform,[10] performed best among five tools evaluated for table extraction.
References
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- 1 2 Douglas Burdick; Marina Danilevsky; Alexandre V Evfimievski; Yannis Katsis; Nancy Wang (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. Wikidata Q108170445.
- ↑ Wenhao Yu; Wei Peng; Yu Shu; Qingkai Zeng; Meng Jiang (2020). "Experimental Evidence Extraction System in Data Science with Hybrid Table Features and Ensemble Learning". Proceedings of The Web Conference 2020: 951–961. doi:10.1145/3366423.3380174. ISBN 978-1-4503-7023-3. Wikidata Q108172460.
{{cite journal}}: CS1 maint: periodical has ISBN (link) - ↑ Benno Kruit; Hongyu He; Jacopo Urbani (2020). "Tab2Know: Building a Knowledge Base from Tables in Scientific Papers". The Semantic Web – ISWC 2020: 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part I. Lecture Notes in Computer Science: 349–365. arXiv:2107.13306. doi:10.1007/978-3-030-62419-4_20. ISBN 978-3-030-62419-4. Wikidata Q101086651.
{{cite journal}}: CS1 maint: periodical has ISBN (link) - ↑ Tobias Bleifuß; Leon Bornemann; Dmitri V. Kalashnikov; Felix Naumann; Divesh Srivastava (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.
- ↑ Sören Auer; Christian Bizer; Georgi Kobilarov; Jens Lehmann; Richard Cyganiak; Zachary Ives (2007). "DBpedia: A Nucleus for a Web of Open Data". The Semantic Web: 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, Busan, Korea, November 11-15, 2007. Proceedings. Lecture Notes in Computer Science: 722–735. doi:10.1007/978-3-540-76298-0_52. ISBN 978-3-540-76297-3. Wikidata Q27910422.
{{cite journal}}: CS1 maint: periodical has ISBN (link) - ↑ Christopher Clark; Santosh Divvala (2016), PDFFigures 2.0: Mining figures from research papers, Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries - JCDL '16, Wikidata Q108172042
- ↑ Andreiwid Sheffer Corrêa; Pär-Ola Zander (7 June 2017), Unleashing Tabular Content to Open Data: A Survey on PDF Table Extraction Methods and Tools, Association for Computing Machinery, doi:10.1145/3085228.3085278, Wikidata Q108173686
- ↑ Meuschke, Norman; Jagdale, Apurva; Spinde, Timo; Mitrović, Jelena; Gipp, Bela (2023), "A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents", in Sserwanga, Isaac; Goulding, Anne; Moulaison-Sandy, Heather; Du, Jia Tina (eds.), Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, vol. 13972, Cham: Springer Nature Switzerland, pp. 383–405, arXiv:2303.09957, doi:10.1007/978-3-031-28032-0_31, ISBN 978-3-031-28031-3
- ↑ "Adobe PDF Extract API". Adobe. Retrieved 2024-03-15.
- ↑ "Experience Cloud AI Services with Adobe Sensei". Adobe. Retrieved 2024-03-15.