TKBG — The knowledge based grep using self-key discovery and semantic linking for online resources

Abstract

The tremendous growth of information resources in the World Wide Web (WWW) made the information retrieval process inefficient and irrelevant due to poor linking of data. One of challenging issues is to find the relevant information from web. Semantic web brings solution by meaningful information retrieval through unique semantic links. In this project, we propose a Self-Key Discovery algorithm based on Sematic Linking Network which allows systematically acquire keys to link RDF(Resource Description Framework) data resources with Semantic relationships among subject Ontologies. Data published on the WWW are usually generated automatically, thus may contain enormous information, duplicates or may be incomplete. RDF is used to represent web content which is the initial stage for semantic linking. SKD algorithm used for RDF data set only, Creation of RDF data is important step to implement semantic linking of data. Proposed Self Key Discovery technique is to performing semantic Linking on knowledge domain clusters using an Ontology Guided Data Linkage (OGDL) framework. This framework allows self-organization of contributing data resources through the discovery of semantic Keys, by performing Linking data of ontological domain knowledge relating to RDF resources. The framework thus automates the discovery of Key to link data across unrelated Resources, and different RDF data set for concept clustering and cluster mapping. In this project, demonstrate the feasibility of our Self Key Discovery algorithms through semantic links to set of RDF Resources, and run on real-world datasets.