T structured semantic weight relationship algorithm combined with decision trees for data extraction

Abstract

In Semantic Web Mining extracting the relevant data from web is the main objective. With the increase in the complexity of the data involved the focus is to achieve better accuracy by extracting the required information. In Semantic web mining the primary input is the user query but the accuracy is based on the domain classification of the data extracted. To achieve accuracy higher a new approach has been proposed where the algorithm same name as article titled maintains a Semantic structure combined with the decision trees. From the training set each word has been tokenized and the relationship between them has been established involving cosine similarity weight as well as to the possibility terms of the same word. Cosine Similarity is calculated not only between the words but also established between the group of the training sentences. The paper explains in detail regarding grouping of training sentences and establishing the weight between them using our proposed approach. © 2019 American Scientific Publishers