Article Code : 13980307189772

Article Title : SGF (Semantic Graphs Fusion): A Knowledge-based Representation of Textual Resources for Text Mining Applications

Journal Number : 26 Spring 2019

Visited : 146

Files : 1.09 MB

List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Morteza Jaderyan Post Graduate Student PhD
2 Hassan Khotanlou Professor PhD


The proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text documents is introduced. The system works by integrating structured knowledge in the core components of the system. Semantic, lexical, syntactical and structural features are identified by the pre-processing module. The enrichment module is introduced to identify contextually similar concepts and concept maps for improving the representation. The information content of documents and the enriched contents are fused (merged) into the graphical structure of semantic network to form a unified and comprehensive representation of documents. The 20Newsgroup and Reuters-21578 dataset are used for evaluation. The evaluation results suggest that the proposed method exhibits a high level of accuracy, recall and precision. The results also indicate that even when a small portion of information content is available, the proposed method performs well in standard text mining applications.