Virtualizing Document Algorithms using Predictive Semantic Data

Semantic web technologies play a vital role in enhancing real-world applications. With the advent of time, information is readily available on the internet in various formats, including files, metadata documents (Microformats, RDF, RDFa), and documents. Often traditional search methods do not offer the adequate and required level of matching users' information with the available online documents, which act as a barrier for efficient usage and reproduction of adapting keywords. This research focuses on an approach that automatically translates user-provided queries into the required formal structured queries. Users can use the approach to perform the translation efficiently. Moreover, the research focuses on the construction of a virtual document and queries for the semantic web data. Other than this, a more advanced search interface with a keyword-based approach is introduced for searching and retrieving most relevant objects.