{"type":"ScholarlyArticle","title":"On Building a Semi-Automated Framework for Generating Causal Bayesian Networks from Raw Text","authors":[{"name":"Solat J. Sheikh","slug":"solat-j-sheikh","given":"Solat J.","family":"Sheikh","affiliation":"Artificial Intelligence Lab , Institute of Business Administration , Karachi , Pakistan"}],"abstract":"The availability of a large amount of unstructured text has generated interest in utilizing it for future decision-making and developing strategies in various critical domains. Despite some progress, automatically generating accurate reasoning models from the raw text is still an active area of research. Furthermore, most proposed approaches focus on a specific do-main. As such, their suggested transformation methods are usually unreliable when applied to other domains. This research aims to develop a framework, SCANER (Semi-automated CAusal Network Extraction from Raw text), to convert raw text into Causal Bayesian Networks (CBNs). The framework will then be employed in various domains to demonstrate its utilization as a decision-support tool. The preliminary experiments have focused on three domains: political narratives, food insecurity, and medical sciences. The future focus is on developing BNs from political narratives and modifying them through various methods to reduce the level of aggressiveness or extremity in the narratives without causing conflict among the masses or countries.","doi":"10.24963/ijcai.2023/822","url":"https://ijcai.org/proceedings/2023/822","pdf":"https://ijcai.org/proceedings/2023/0822.pdf","markdown":"https://ijcai.org/proceedings/2023/822.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/822","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":822,"pages":{"first":7095,"last":7096},"keywords":["Natural Language Processing: NLP: Applications","Uncertainty in AI: UAI: Graphical models","Knowledge Representation and Reasoning: KRR: Causality","Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[{"position":1,"raw":"[Doan et al., 2019] Doan, Son, Elly W Yang, Sameer S Tilak, Peter W Li, Daniel S Zisook, and Manabu Torii. 2019. \"Ex-tracting Health-Related Causality from Twitter Mes- sages Us-ing Natural Language Processing.\" BMC Medi- cal Informatics and Decision Making 19 (3): 71-77.","title":"Ex-tracting Health-Related Causality from Twitter Messages Us-ing Natural Language Processing","authors":"Son Doan, Elly W Yang, S Sameer, Peter W Tilak, Daniel S Li, Manabu Zisook, Torii","year":"2019","venue":"BMC Medical Informatics and Decision Making","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Levis, 2019] Levis, Alexander H. 2019. \"On Narrative Model-ing And Assessment For Strategic Change.\" In ECMS, 393-99.","title":"On Narrative Model-ing And Assessment For Strategic Change","authors":"Alexander H Levis","year":"2019","venue":"ECMS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Min et al., 2021] Min, Bonan, Benjamin Rozonoyer, Haoling Qiu, Alexan-der Zamanian, and Jessica MacBride. 2021. \"Excava-torcovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for Covid-19.\" ArXiv Preprint ArXiv:2105.01819.","title":"Excava-torcovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for Covid-19","authors":"Min","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Sharp et al., 2019] Sharp, Rebecca, Adarsh Pyarelal, Benja- min Gyori, Keith Alcock, Egoitz Laparra, Marco A Valenzuela-Escárcega, Ajay Nagesh, et al. 2019. \"Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models.\" In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computa- tional Linguistics (Demonstrations). Figure 1: Structure of the Framework (SCANER) for Construct- ing Causal Bayesian Networks","title":"Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models","authors":"Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A Valenzuela-Escárcega, Ajay Nagesh","year":"2019","venue":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). Figure 1: Structure of the Framework (SCANER) for Constructing Causal Bayesian Networks","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}