Focused Retrieval of E-books using Text Learning and Semantic Search

Abstract

There are many online digital libraries (DLs) containing books, authors and subjects' data, which are accessed via internal search services as well as external web sites such as Google, Yahoo. Due to a continuous rising of heterogeneous data on the Web it is difficult for users to access relevant information online. Digital Libraries face similar challenges due to fast growth of available electronic data. Increase availability of users to access full-text of digitized books on the Web it prompts Digital Libraries to enhance usability and the searching techniques to obtain highly relevant and more focused results (e.g. Books or answers to a user query). The traditional Information Retrieval methodologies generally retrieved documents by matching terms in documents that contains user specified keywords and the term with higher frequency. The proposed system solves the problem by using ontology, metadata and semantic search. This model consists of identifying concepts in users' query and expands them by creating ontology from text learning. The proposed system aims to improve usability, relevancy ratio with respects to accessing books from Digital Libraries based on user query. The proposed system will retrieve particular chapters based on the user query.