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

Seethalakshmi Gopalakrishnan, Victor Zitian Chen, Wenwen Dou, Gus Hahn-Powell, Sreekar Nedunuri, Wlodek Zadrozny

Research output: Contribution to journalArticlepeer-review

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).

Original languageEnglish (US)
Article number367
JournalInformation (Switzerland)
Volume14
Issue number7
DOIs
StatePublished - Jul 2023

Keywords

  • causality extraction
  • natural language processing (NLP)
  • organizational data
  • stakeholder taxonomy

ASJC Scopus subject areas

  • Information Systems

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