Over the recent years smart devices have become a ubiquitous medium supporting various forms of functionality and are widely accepted for common users. One distinguishing feature for smart devices is the ability of positioning the physical location of a device, and numerous applications based on user location information have been proposed. While the potentials have been foreseen, location based services fundamentally suffer from the problem of lacking an effective and scalable mechanism to bridge the gap between the machine-observed locations and the human understandable places. In this study, we contribute on this fundamental problem. Differing from the existing solutions on this subject, we start from a novel perspective; we propose to address the place semantic understanding problem by casting it as a classification problem and employ machine learning techniques to automatically infer the types of the places. The key observation is that human behaviors are not random, e.g., people visit restaurants around noon, go for work in the daytime, and stay at home at night. Namely, by properly selecting features, a mechanism for automatically inferring place type semantics can be achieved. This paper summarizes our treatment and findings of leveraging the human behaviors patterns to infer the type of a place. Experiments using month-long trace logs from the recruited participants are conducted, and the experiment results demonstrate the effectiveness of the proposed method.