A satisfactory and robust trust model is gaining importance in addressing information overload, and helping users collect reliable information in online communities. Current research on trust prediction strongly relies on a web of trust, which is directly collected from users based on previous experience. However, the web of trust is not always available in online communities and even though it is available, it is often too sparse to predict the trust value between two unacquainted people with high accuracy. In this paper, we propose a framework to derive degree of trust based on users' expertise and users' affinity for certain contexts (topics), using users rating data which is available and much more dense than direct trust data. In experiments with a real-world dataset, we show that our model can predict trust connectivity with a high degree of accuracy. With this framework, we can predict trust connectivity and degree of trust without a web of trust and then apply it to online community applications, e.g. e-commerce environments with users rating data.