Semantic inference method using ontologies

Hadoop is an open source file system that can have a framework is processed over the big data. The fast growth of ontologies nowadays that can grow significantly performs normally and also some major issues in the efficiency and scalability reasoning methods. The traditional and centralized reasoning methods do not handle large ontologies. The system proposed a large scale ontologies for healthcare is applied to use map reduce and hadoop framework. Semantic inference method attracts much attention of users from all fields. Many inference engines have been developed to support the reasoning over semantic web. The system also proposed a transfer inference forest and effective assertional triples for reduce the storage for reasoning methods and also simplified and accelerate. The Ontology web language provides the semantic web access to all the relationships maintained by the syntaxes, specifications and expressions. With a large volume of Semantic Web data and their fast growth, diverse applications have emerged in a plurality of domains poses new challenges for ontology mapping. Ontology mapping can provide more correct results if the mapping process can deal with uncertainty effectively that is caused by the incomplete and inconsistent information used and produced by the mapping process. As it is evolving into a global knowledge-based framework, supporting knowledge searching over such a big and increasing dataset has become an important issue. A survey was made for different reasoning approaches that focus on semantic inferences. This paper describes about how the reasoning approaches process on users' queries. This paper proposes an incremental and distributed inference method for large-scale Ontologies by using MapReduce, which realizes highperformance reasoning and runtime searching, especially for incremental knowledge base. By constructing transfer inference forest and effective assertional triples, the storage is largely reduced and the reasoning process is simplified and accelerated.