SGF (Semantic Graphs Fusion): A Knowledge-based Representation of Textual Resources for Text Mining Applications
محورهای موضوعی : Natural Language ProcessingMorteza Jaderyan 1 , Hassan Khotanlou 2
1 - Bu Ali SIna University
2 - Bu Ali Sina Uinversity
کلید واژه: Semantic document representation, , Ontology, , Knowledge base (KB), , Semantic network, , Information fusion, ,
چکیده مقاله :
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 analysis applications is introduced. The proposed functionalities of the system are achieved by integrating structured knowledge in the core components of the system. The 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 then fused (merged) into the graphical structure of a semantic network to form a unified and comprehensive representation of documents. The 20Newsgroup and Reuters-21578 datasets 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 the information content is available, the proposed method performs well in standard text mining applications
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.
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