Ontology Based Effective Semantic Information Retrieval for Big Data

A huge amount of data stored on the Internet will be useful and helpful only if it is accessed as information, not as pure data. Nowadays Big data overcomes several issues such as searching, analysing, sharing, storage, transfer, visualization and querying. Among these issues, semantic retrieval is a huge issue. In order to avoid these problems, Hadoop Distributed File System (HDFS) is proposed. HDFS performs semantic analysis over the volume of documents (Big data) to find the best matched source document from the collected set of source documents for the same virtual document. In the hadoop file system, the semantic analysis is done using Dual Walk based Ranking model for providing best matched documents and the resulting documents are filtered by making use of Top K algorithm based on the frequency of the entities in the source document. But, the existing system still has issues with the ontological indexing concept and hence the accuracy of semantic information retrieval is reduced. In order to overcome this ontological indexing concept is focused to retrieve highly relevant and semantic information. Ontology based information retrieval increases the most relevant information by filtering the unrelated terms in the documents. The documents are clustered based on the Ontology and the input query is examined for semantics and expanded using domain Ontology. Thus the accuracy of the semantic information is increased and searching complexity is reduced significantly. From the experimental result, the conclusion decides that the proposed system is better than the existing system.