Enhancing data quality by cleaning inconsistent big RDF data

We address the problem of dealing with inconsistencies in fusion of big data sources using Resource Description Framework (RDF) and ontologies. We propose a scalable approach ensuring data quality for query answering over big RDF data in a distributed way on a Spark ecosystem. In so doing, the cleaning inconsistent big RDF data approach is built on the following steps (1) modeling consistency rules to detect the inconsistency triples even if it is implicitly hidden including inference and inconsistent rules (2) detecting inconsistency through rule evaluation based on Apache Spark framework to discover the minimally sub-set of inconsistent triples (3) cleaning the inconsistency through finding the best repair for consistent query answering.