TY - JOUR
T1 - Text to Causal Knowledge Graph
T2 - A Framework to Synthesize Knowledge from Unstructured Business Texts into Causal Graphs
AU - Gopalakrishnan, Seethalakshmi
AU - Chen, Victor Zitian
AU - Dou, Wenwen
AU - Hahn-Powell, Gus
AU - Nedunuri, Sreekar
AU - Zadrozny, Wlodek
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/7
Y1 - 2023/7
N2 - 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).
AB - 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).
KW - causality extraction
KW - natural language processing (NLP)
KW - organizational data
KW - stakeholder taxonomy
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U2 - 10.3390/info14070367
DO - 10.3390/info14070367
M3 - Article
AN - SCOPUS:85166396698
SN - 2078-2489
VL - 14
JO - Information (Switzerland)
JF - Information (Switzerland)
IS - 7
M1 - 367
ER -