Determining semantic relatedness between words or concepts is a fundamental process to many Natural Language Processing applications. Approaches for this task typically make use of knowledge resources such as WordNet and Wikipedia. However, these approaches only make use of limited number of features extracted from these resources, without investigating the usefulness of combining various different features and their importance in the task of semantic relatedness. In this paper, we propose a random walk model based approach to measuring semantic relatedness between words or concepts, which seamlessly integrates various features extracted from Wikipedia to compute semantic relatedness. We empirically study the usefulness of these features in the task, and prove that by combining multiple features that are weighed according to their importance, our system obtains competitive results, and outperforms other systems on some datasets.