Tools & Data
All pointers to the developed resources
FARO: Facts and Events Relationship Ontology
FARO provides a shared vocabulary for representing how events and conditions are connected in knowledge graphs. It makes relations such as causing, enabling, preventing, and intending explicit, structured, and machine-readable.
Causal Reasoner for Fact-Checking
This explainable fact-checking pipeline goes beyond verdict prediction. It extracts event relations from claims and evidence, combines them with similarity and polarity signals, and applies reasoning rules to make the causal logic behind a verdict inspectable.
View the Fact-Checking Reasoner on GitHub
CausalSense: Refined Causality Extraction from Text
CausalSense is an open dataset, model collection, and pipeline for extracting fine-grained relationships between events. It combines news data, common-sense knowledge, and synthetic data to identify whether events cause, enable, prevent, or intend one another.