This research aims at focusing on building a knowledge base shell which task is to automatically build the knowledge base component from text sources. The proposed research aims at considering the Arabic text sources as one of the main challenges in due to the difficulty in processing Arabic text that suffers from the high inflection, shortcuts, and the varieties in letters' representation. The research restored the confidence in the term frequency method and the research adopts the semantic relations network representation. The research applied the experiment for building the weeds' identification facts' part in the knowledge-based system. The experiment applied the proposed framework on two sources, the precision, recall, and f-score have been measured which resulted in an average of 91.2%, 91.1%, and 91% respectively. The evaluation metrics provide a promising perspective in the proposed approach with more focus on the bottlenecks for enhancements.
One of the current essential methods for communication is electronic email (e-mail). It is currently considered the official method for different business activities such as conducting agreements, the setup of official meetings, and team collaboration. This continuous interest in e-mails as a communication channel has drawn the attention to the need for eliminating spam which have a vital effect on both network resources and business activities. This research focuses on generating a ham-spam lexicon based on text analysis which is aimed to be one of the main resources for detecting personal spam e-mails. The lexicon generation is a key step to efficiently and economically successful spam elimination. The proposed framework has proven its applicability on a dataset of six groups and the classification algorithms have been examined to prove the efficient classification. The research is a step in a wider view for general intelligent business communication and collaboration framework.
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