# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 2 Literature Review
### Literature Review on Arabic Ontology Learning  
Ontology learning from text is a very important area in computer science. Published works on ontology learning from Arabic texts are still rare. As previously mentioned, the contributions of the state-of-the-art Arabic ontology learning from texts can be distinguished into one of the following categories. The works under these categories were examined in the following section and using Table 2.  
The rule-based approaches [12; 20; 60; 61; 62; 21] rely on patterns comprising all the possibly-correlated linguistic sequences commonly executed in a form of finite-state transducers or even regular expressions. Despite those methods being beneficial for a limited domain, besides their better analysis quality, they cannot act in a good way, in particular, the creation of the manually hand-crafted patterns is so laborious with regard to effort and time. Hence, through the applications of such approaches, it is difficult to manipulate enormous amounts of data. For automating the relations extraction, some studies [9; 19; 63; 64; 22] have used machine learning algorithms involving (1) unsupervised, (2) semi-supervised, and (3) supervised learning. For the unsupervised methods, the popular approach takes clusters from the patterns of the same relationship and then generalizes them. However, the semantic representations of relational patterns, in addition to the scalability to big data, make these methods face a challenge in reference to the reliability of the obtained patterns [55]. Although these algorithms can manipulate large quantities of data, the conversion of the output relations to ontologies represents a labor-intensive task. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** &
\begin{tabular}{c} **Year of** \\ **Publication** \\ \end{tabular} & **Arabic Text Mining Algorithm** & **Corpus** & **Accuracy** \\ \hline
[53] & 2014 & \
\begin{tabular}{c} Cosine Coefficient, Jacaard \\ Coefficient, and Dice Coefficient \\ \end{tabular} & Saudi Newspapers (SNP) &
\begin{tabular}{c} Cosine coefficient outperformed \\ Jaccard and Dice coefficients \\ with 0.917, 0.979, and 0.947 for \\ Precision, Recall, and \(F\)-measure, \\ respectively. \\ \end{tabular} \\ \hline
[54] & 2014 & \
\begin{tabular}{c} Term frequency (TF), and Term \\ Frequency/Inverse Document \\ Frequency (TF/IDF) for feature \\ extraction, Semi-Automatic \\ Categorization (SAC), and Automatic \\ Categorization (AC) for feature \\ selection. \\ \end{tabular} & News books: Arabic &
\begin{tabular}{c} Global recognition score is used \\ to measure the ratio of \\ correctly-classified documents: \\ employing TF/IDF (95\%), and \\ TF (88\%) \\ \end{tabular} \\ \hline
[55] & 2015 &
\begin{tabular}{c} TF/IDF, Chi Square for selecting \\ feature, besides a local class-based \\ policy for feature reduction \\ \end{tabular} & Al-Jazeera News & Recall of 0.967\%, and \(F\)-measure \\ \hline
[56] & 2017 & Latent Semantic Indexing & Alqabas newspaper in \\ Kuwait & & 82.50\% \\ \hline
[57] & 2018 & Vector Space Model (VSM) &
\begin{tabular}{c} Set by Alqabas newspaper, \\ in Kuwait \\ \end{tabular} & 84.4\% \\ \hline
[58] & 2018 & VSM & Alqabas newspaper & 90.29\% \\ \hline
[59] & 2019 & \
\begin{tabular}{c} Removing the stop words existed in \\ the collected tweets, extracting the \\ keywords and sorting them into one \\ of the corresponding categories: \\ classified words or unclassified words. \\ Then, applying named entity \\ recognition as well as data analysis \\ rules on the classified words to \\ generate final report. The lexical \\ features along with Twitter-specific \\ features were employed in \\ classification. \\ \end{tabular} &
\begin{tabular}{c} A private database of \\ collected Arabic tweets \\ \end{tabular} & 96.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A state-of-the-art on Arabic text mining.