Retrieval efficiency of temporal issues can be enhanced by getting into account the time aspect. Latest temporal ranking systems use a couple of main strategies: 1) a mixture unit linearly integrating textual resemblance as well as temporal resemblance, and 2) a probabilistic system producing a query from the textual as well as temporal component of report automatically. In this document, we suggest a unique time-aware ranking system according to learning-to-rank strategies. We use two classes of attributes for understanding a ranking system, entity-based as well as temporal attributes, which are based on annotation information. Entity-based attributes are targeted at acquiring the semantic resemblance around a query as well as a document, while temporal attributes determine the temporal resemblance. By using considerable studies we reveal that our ranking system considerably enhances the retrieval efficiency over current time-aware ranking systems.