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KIETA: Key-insight extraction from scientific tables
Please always quote using this URN: urn:nbn:de:bvb:20-opus-324180
- An important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting themAn important but very time consuming part of the research process is literature review. An already large and nevertheless growing ground set of publications as well as a steadily increasing publication rate continue to worsen the situation. Consequently, automating this task as far as possible is desirable. Experimental results of systems are key-insights of high importance during literature review and usually represented in form of tables. Our pipeline KIETA exploits these tables to contribute to the endeavor of automation by extracting them and their contained knowledge from scientific publications. The pipeline is split into multiple steps to guarantee modularity as well as analyzability, and agnosticim regarding the specific scientific domain up until the knowledge extraction step, which is based upon an ontology. Additionally, a dataset of corresponding articles has been manually annotated with information regarding table and knowledge extraction. Experiments show promising results that signal the possibility of an automated system, while also indicating limits of extracting knowledge from tables without any context.…
Author: | Sebastian KempfORCiD, Markus Krug, Frank Puppe |
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URN: | urn:nbn:de:bvb:20-opus-324180 |
Document Type: | Journal article |
Faculties: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Language: | English |
Parent Title (English): | Applied Intelligence |
ISSN: | 0924-669X |
Year of Completion: | 2023 |
Volume: | 53 |
Issue: | 8 |
Pagenumber: | 9513-9530 |
Source: | Applied Intelligence (2023) 53:8, 9513-9530 DOI: 10.1007/s10489-022-03957-8 |
DOI: | https://doi.org/10.1007/s10489-022-03957-8 |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
Tag: | information extraction; key-insight extraction; ontology; table extraction; table understanding |
Release Date: | 2024/01/17 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |