A Review of Large-Scale RDF Document Processing in Hadoop MapReduce Framework

Resource Description Framework (RDF) is Meta data model which can be used to store Meta data information of various large complex datasets which can further be used to extract or infer some meaningful out of it. To process these vast amount of data and infer some reasoning out of these is tedious task. Traditional centralized reasoning methods are not sufficient to process large ontologies. Distributed reasoning methods are thus required to improve the scalability and performance of inferences. MapReduce is widely used parallel computing model which able to process these huge amount of data in no time. Various methods are introduced to process RDF document. This paper reviews various methods and techniques used for processing RDF documents. Keywords— Information retrieval, RDF Document, Semantic Information, XML, MapReduce, Hadoop.