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The Scientific World Journal
Volume 2015, Article ID 136172, 19 pages
Research Article

Exploiting Semantic Annotations and -Learning for Constructing an Efficient Hierarchy/Graph Texts Organization

Department of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt

Received 15 July 2014; Revised 10 December 2014; Accepted 10 December 2014

Academic Editor: Miguel-Angel Sicilia

Copyright © 2015 Asmaa M. El-Said et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper proposes an efficient methodology for constructing hierarchy/graph-based texts organization and representation scheme based on semantic annotation and -learning. This methodology is based on semantic notions to represent the text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. The representation scheme reflects the existing relationships among concepts and facilitates accurate relatedness measurements that result in a better mining performance. An extensive experimental evaluation is conducted on real datasets from various domains, indicating the importance of the proposed approach.