Predicting future events from text data has been a controversial and much disputed topic in the field of text analytics. However, far too little attention has been paid to efficient prediction in textual environments. This study has aimed to develop a novel and efficient method for news event prediction. The proposed method is based on Markov logic networks (MLNs) framework, which enables us to concisely represent complex events by full expressivity of first-order logic (FOL), as well as to reason uncertain event with probabilities. In our framework, we first extract text news events via an event representation model at a semantic level and then transform them into web ontology language (OWL) as a posteriori knowledge. A set of domain-specific causal rules in FOL associated with weights were also fed into the system as a priori (common-sense) knowledge. Additionally, several large-scale ontologies including DBpedia, VerbNet and WordNet were used to model common-sense logic rules as contextual knowledge. Finally, all types of such knowledge were integrated into OWL for performing causal inference. The resulted OWL knowledge base is augmented by MLN, which uses weighted first-order formulas to represent probabilistic knowledge. Empirical evaluation of real news showed that our method of news event prediction was better than the baselines in terms of precision, coverage and diversity.