Data is often associated with uncertainty because the fusion of conflicting data sources, measurement inaccuracy, sampling discrepancy, outdated data sources. We address the problem of query answering over uncertain big data sources using Resource Description Framework (RDF) and ontologies, while computing the exact uncertainty measure of the answer. Therefore, the probability is embraced along the reasoning process when answering. the query. In this paper, we introduce a probabilistic approach for answering user queries that computes complete results by exploiting uncertain knowledge on data sources. We have designed algorithms that are ontological rules based to infer implicit data by combining saturation and query rewriting reasoning. To handle big data the algorithms are spark-based implementation.