Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Seethalakshmi Gopalakrishnan
,
Victor Z. Chen
,
Wenwen Dou
ORCID logo ,
Gus Hahn-Powell
ORCID logo ,
Sreekar Nedunuri
ORCID logo ,
Wlodek W. Zadrozny
* ORCID logo
Version 1 : Received: 9 May 2023 / Approved: 10 May 2023 / Online: 10 May 2023 (13:36:31 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

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 the relevance to different stakeholder groups’ 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 textual data, in order to inform and even prescribe the best actions that may affect target business outcomes related to different stakeholders’ benefits (customers, employees, investors, and the community/environment).

Keywords

Causality extraction; Organizational data; Stakeholder Taxonomy; Natural Language Processing; NLP

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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