Text to Causal Knowledge Graph: A Framework to Synthesize Knowledge from Unstructured Business Texts into Causal Graphs
Name:
information-14-00367.pdf
Size:
740.4Kb
Format:
PDF
Description:
Final Published Version
Affiliation
Department of Linguistics, University of ArizonaIssue Date
2023-06-28Keywords
causality extractionnatural language processing (NLP)
organizational data
stakeholder taxonomy
Metadata
Show full item recordCitation
Gopalakrishnan, S.; Chen, V.Z.; Dou, W.; Hahn-Powell, G.; Nedunuri, S.; Zadrozny, W. Text to Causal Knowledge Graph: A Framework to Synthesize Knowledge from Unstructured Business Texts into Causal Graphs. Information 2023, 14, 367. https://doi.org/10.3390/info14070367Journal
Information (Switzerland)Rights
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
This article presents a state-of-the-art system to extract and synthesize causal statements from company reports into a directed causal graph. The extracted information is organized by its relevance to different stakeholder group benefits (customers, employees, investors, and the community/environment). The presented method of synthesizing extracted data into a knowledge graph comprises a framework that can be used for similar tasks in other domains, e.g., medical information. The current work addresses the problem of finding, organizing, and synthesizing a view of the cause-and-effect relationships based on textual data in order to inform and even prescribe the best actions that may affect target business outcomes related to the benefits for different stakeholders (customers, employees, investors, and the community/environment). © 2023 by the authors.Note
Open access journalISSN
2078-2489Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.3390/info14070367
Scopus Count
Collections
Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license.