Resource Description Framework (RDF) has been used as a general model for conceptual description and information modelling. As the growing number and volume of RDF datasets emerged recently, many techniques have been developed for accelerating the query answering process on triple stores, which handle large-scale RDF data. Caching is one of the popular solutions. Non-RDBMS based triple stores, which leverage the intrinsic nature of RDF graphs, are emerging and attracting more research attention in recent years. However, as their fundamental structure is different from RDBMS triple stores, they can not leverage the RDBMS caching mechanism. In this paper, we develop a time-aware frequency based caching algorithm to address this issue. Our approach retrieves the accessed triples by analyzing and expanding previous queries and collects most frequently accessed triples by evaluating their access frequencies using Exponential Smoothing, a forecasting method. We evaluate our approach using real world queries from a publicly available SPARQL endpoint. Our theoretical analysis and empirical results show that the proposed approach outperforms the state-of-the-art approaches with higher hit rates.