Semantic Model for Web-Based Big Data Using Ontology and Fuzzy Rule Mining

A huge amount of data is being generated everyday through different transactions in industries, medicals, social networking, communication systems etc. This data is mainly of unstructured format in nature. Transformation of the large heterogeneous datasets into useful information is very much required for society. This huge unstructured information should be easily presented and made available in a significant and effective way to obtain semantic knowledge so that machine can interpret them. In this paper, we have introduced a novel approach for semantic analysis with web based big data using rule based ontology mapping. To handle social data with natural language terms, we have proposed the fuzzy rule based resource representation. After that, a refined semantic relation reasoning mining is applied to obtain overall knowledge representation. Finally semantic equivalent of these unstructured data is stored in structured database using Web Ontology Language (OWL) based ontology system.