An Incremental and Distributed Inference Method for Large,..A.Vijayalakshmi,et al,.

Reasoning on a Web scale becomes increasingly challenging because of the large volume of data involved and the complexity of the task by means of ontology mapping. Here, an IDIM concept is used to deal with largescale incremental RDF datasets. Resource Description Framework (RDF) is an important data, presenting standard of the semantic web to process the increasing RDF data. Map Reduce is a widely-used parallel programming model that can be used to represent uncertain similarities created by both syntactic and semantic similarity algorithms. The proposed OneClass Clustering Tree (OCCT) characterizes the entities by identifying different entries that should be linked together. The construction of TIF and EAT significantly reduces the recomputation time for the incremental inference as well as the storage for RDF triples. Therefore, users can execute their query more efficiently without computing and searching over the entire RDF closure used in the prior work. The final results are evaluated by comparing it against benchmark models in web information gathering.