Building a Knowledge Base Shell Based on Exploring Text Semantic Relations from Arabic Text

Abstract

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.

A Proposed Deep Learning based Framework for Arabic Text Classification

“…Term frequency is concerned with how frequently a word or a combination of words occurs in a detected one document. Where TF-IDF is concerned with how frequently a word or a combination of words occurs within the overall document [28].…”

“…The problems of classifying texts are not only represented in the tremendous diversity in the use of cognitive sources or in the momentum but also the abundance of information and circulating data, which sometimes reach millions of terabytes. Moreover, there is a crucial factor which is the language in which these texts are written [28].…”