Improving ontology and precision recall using ontology model, genetic, greedy algorithm semantic similarity calculation and ontology graph

The content is extracted by means of semantic relevancy. The semantic relevancies relate the content of videos based on a certain parameter. The parameter varies between system to system (implementation). The parameter will improve the performance of semantic relevancy and accuracy. This accuracy is obtained after various random experiments. Here a method called concept, sub concept graph method is used to implement the semantic relevancies. A graph algorithm is constructed to improve the relevancies between concepts. The ontology model is created based on the relationship between the vertices. At first relationship between the parent and child are calculated. Then based on all the relationships the diagrammatic representations are done. Based on hit rates the priority of web pages are done and based on the number of relationships the value for the vertices is noted.