Keyword searches are a good substitute for a subject search. It is useful when you do not know the standard subject heading. Many large-scale RDF knowledge bases with billions of facts are generating due to the growth of semantic data on the Web. This poses significant challenges for the storage and query of big RDF graphs. It is used for exploring large RDF (Resource Description Framework) Graph which is used as a W3C standard to describe data in the Semantic Web for many real time applications. In Existing techniques, RDF Data may often suffer from the unreliability of their data sources, and exhibit errors or inconsistencies. In this paper, we overcome these kinds of inconveniences with the help of an important problem, keyword search query over probabilistic RDF graphs (namely, the pg-KWS query) by probabilistic RDF graphs. To retrieve meaningful keyword search answers, we design the score rankings for sub graph answers specific for RDF data. Furthermore, we propose effective pruning methods to quickly filter out false alarms. We construct an index over the pre-computed data for RDF. We also present an efficient query answering approach through the index. Experiments have been conducted to verify the proposed approaches.