Machine Learning Based Annotating Search Results from Web Databases
|P.Renukadevi1, K.Priyanka1, D.Shree Devi1, H.Abhijith1, T.Yogananth2, A.M.Ravishankkar2 and Dr.S.Rajalakshmi3
|Related article at Pubmed, Scholar Google|
Deep web is a database based, i.e., for many search engines, data encoded in the returned result pages come from the underlying structured databases. Such type of search engines is often referred as Web databases (WDB). A typical result page returned from a WDB has multiple search result records (SRRs). Unfortunately, the semantic labels of data units are often not provided in result pages. Having semantic labels for data units is not only important for the above record linkage task, but also for storing collected SRRs into a database table. Early applications require tremendous human efforts to annotate data units manually, which severely limit their scalability. In this paper, we consider how to automatically assign labels to the data units within the SRRs returned from WDBs improve the results with new kernel function for improving the accuracy of the Support Vector Machines (SVMs) classification. The proposed kernel function is stated in general form and is called Gaussian Radial Basis Polynomials Function (GRPF) that combines both Gaussian Radial Basis Function (RBF) and Polynomial (POLY) kernels. We implement the proposed kernel with a number of parameters associated with the use of the SVM algorithm that can impact the results.