  In recent years, the size of big linked data has grown rapidly and this
number is still rising. Big linked data and knowledge bases come from different
domains such as life sciences, publications, media, social web, and so on.
However, with the rapid increasing of data, it is very challenging for people
to acquire a comprehensive collection of cross domain knowledge to meet their
needs. Under this circumstance, it is extremely difficult for people without
expertise to extract knowledge from various domains. Therefore, nowadays human
limited knowledge can't feed the high requirement for discovering large amount
of cross domain knowledge. In this research, we present a big graph analytics
framework aims at addressing this issue by providing semantic methods to
facilitate the management of big graph data from close domains in order to
discover cross domain knowledge in a more accurate and efficient way.
