News Events Prediction Based on Casual Inference in FirstOrder Logic (FOL)

A novel method for future event prediction is proposed in textual environment. Proposed method is able to produce an event prediction model through generalization of cause events and then predict the effect events by using causal rules. First, the events of interest are extracted from domainspecific texts via an event representation model at semantic level, and are stored in the form of a graphical model in ontology as a posteriori (dynamic) knowledge. Then, a set of domain-specific causal rules in first-order logic (FOL) are fed into the machine as a priori (common-sense) knowledge. In addition to this common-sense knowledge, several large-scale ontologies containing DBpedia, VerbNet and WordNet are used for modeling contextual (static) knowledge and generalizing events. Finally, all types of these knowledge are integrated in a standard Web ontology Language (OWL) to perform causal inference. Empirical evaluation on real news articles showed that our method was better than the baselines.