This research addresses the scientific challenge of accurately capturing event flows from textual data, crucial for informed decision-making, historical analysis, and predictive modeling.
mso-ansi-language:EN-US">We introduce FARO, an ontology that structures 25 distinct relationships among events and facts, enabling richer semantic representations. To support robust event relation extraction, we leverage large language models (LLMs), common sense knowledge from the ATOMIC knowledge graph, and generative AI techniques to create a novel annotated dataset of over 500,000 sentences encompassing refined relations such as direct causality, enablement, prevention, and intention. Utilizing this comprehensive resource, we develop and comparatively evaluate an advanced extraction model capable of identifying fine-grained causal event relationships. The practical effectiveness of our approach is validated through two applications: enhanced narrative generation via structured and context-rich knowledge graphs, and automated fact-checking using causal reasoning, achieving a notable F1-score of 61.56% on the AVeriTeC dataset, thus establishing a strong foundation for future research in causal-aware Methodologies.
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