Automated Ontology Integration And Semantic Annotation For Semantic Web

The conceptual view of semantic web is 'web of documents' processed by software agents to dig out the web of things.To achieve certain goals on existing web,semantic annotation and ontology integration plays a vital role.However,recent studies suggest that semantic web is not yet fully implemented because there are many obstacles.Most important ones are the automatic semantic ontology integration and semantic annotation of big data for semantic web.To overcome these obstacles,this study proposes a framework for the automated semantic concept matching for ontology integration and for the big data localization and semantic annotation.The usage of multiple ontologies of the same domain may raise the problem of heterogeneity between ontologies.The ontology integration delivers a solution to the heterogeneity problem.In this work,we investigate the concept matching process of ontology integration,which causes the heterogeneity problem,and propose an automated approach for semantic comparison of concept matching.The proposed approach employs natural language processing techniques to avoid the vocabulary or corpus for semantic matching of concepts which is a major limitation of state of the art approaches.Thus the exception from this work is a more robust intelligent state-of-the-art framework for the ontology integration.On the other hand most of the data concerning business-oriented systems are still based on either NoSQL or the relational data model.Where's now a days,Semantic Web data model RDF has become the new standard for data modeling and analysis.Due to this situation integration of NoSQL,RDB and RDF data models is becoming a required feature of the systems.The aim of study is to compare and map data models used for transformation between NoSQL,RDB and Semantic Web.This study helps in achieving much better and enhanced technology-based systems for retrieval and storage of data among Big-data data annotation using Semantic Web.In the context of natural-language processing,keyword extraction has been studied widely.In promoting business enterprise goods and web services,keyword extraction is a crucial component of many knowledge-based applications such as automatic indexing,knowledge discovery,terminology mining and monitoring,knowledge management and so on.However,a major challenge remains to extracting keywords effectively and efficiently from social-media user-generated data,wherein employed are traditional,language-dependent and supervised keyword-extraction techniques.This study contributes a keyword extraction for ontology building using analytic hierarchy process(KEAHP),as a language-independent and unsupervised keyword-extraction technique.