Text to Causal Knowledge Graph: A Framework to Synthesize Knowledge from Unstructured Business Texts into Causal Graphs

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/environme...

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Bibliographic Details
Published in:Information (Basel) 2023-07, Vol.14 (7), p.367
Main Authors: Gopalakrishnan, Seethalakshmi, Chen, Victor Zitian, Dou, Wenwen, Hahn-Powell, Gus, Nedunuri, Sreekar, Zadrozny, Wlodek
Format: Article
Language:English
Subjects:
Algorithms
American dollar
Annotations
Annual reports
Business metrics
Causality
causality extraction
Company structure
Customers
Datasets
Deep learning
Employee benefits
Foreign exchange rates
Graduate students
Knowledge
Knowledge representation
Machine learning
natural language processing (NLP)
organizational data
Performance management
Social responsibility
stakeholder taxonomy
Stakeholders
Synthesis
Taxonomy
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Summary: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).
ISSN:2078-2489
2078-2489
DOI:10.3390/info14070367