Temporal analysis of events is a central problem in computational models of dis-course. However, correctly recognizing temporal aspects of events poses serious challenges. This paper introduces a joint modeling framework and feature set for temporal analysis of events that utilizes Markov Logic relational learning. The feature set includes novel features derived from lexical ontologies. An evaluation suggests that introducing lexical relation features improves the overall accuracy of temporal relation models.