Previous Chinese argument extraction approaches mainly focus on feature engineering and trigger expansion, which cannot exploit inner relation between trigger mentions in same document. To address this issue, the authors bring forward a novel trigger inference mechanism based on Markov logic network. Head morpheme, the probabilities of a trigger mention fulfilling true and pseudo events from the training set and the relationships between trigger mentions are used to infer those trigger mentions with lack of effective context information or low confidences in testing set. Experimental results on the ACE 2005 Chinese corpus show that the proposed approach outperforms the baseline, with the F1 improvements of 3.65% and 2.51% in trigger identification and event type classification respectively.