# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 2 Literature Review
### Literature Review on Arabic Ontology Learning
\begin{table}
\begin{tabular}{p{56.9 pt} p{34.1 pt} p{34.1 pt} p{34.1 pt}} \hline \hline
**Methodology** & **Works** &
\begin{tabular}{c} **Year of** \\ **Publication** \\ \end{tabular} & \multicolumn{1}{c}{**Contribution**} \\ \hline \multirow{6}{*}{Rule-based approaches} & [60; 61] & 2010 \& 2012 & Extracting a set of linguistic patterns from text then rewriting it into finite state transducers \\ \cline{2-4}  & \multirow{3}{*}{[12]} & \multirow{3}{*}{2016} & The authors developed a model of pattern recognizer that targets to signal the existence of cause–effect information in sentences from non-specific domain texts. The model incorporated 700 linguistic patterns to distinguish the sentence parts representing the cause, besides to these representing the effect. To construct patterns, various sets of the syntactic features were considered through analyzing the untagged corpus. \\ \hline \multirow{6}{*}{[62]} & \multirow{3}{*}{2017} & \multirow{3}{*}{2017} & The authors introduced a rule-based system namely, ASRextractor, to extract and annotate semantic relations relating Arabic named entities. The semantic relation extraction was based upon an annotated corpus of Arabic Wikipedia. The corpus incorporated 18 types of semantic relations like synonymy and origin. \\ \hline \multirow{6}{*}{[20]} & \multirow{3}{*}{2018} & A statistical parsing method was adopted to estimate the key-phrase/keyword from the Arabic dataset. The extracted dataset was converted to an OWL ontology format. Then, the mapping rules were used to link the components of ontology to corresponding keywords. \\ \hline \multirow{6}{*}{[21]} & \multirow{3}{*}{2018} & A set of rules/conjunctive patterns were defined for extracting the semantic relations of the Quranic Arabic according to a deep study for Arabic grammar, POS tagging, as well as the morphology features appears in the corpus of Quranic Arabic. \\ \hline \multirow{6}{*}{[63]} & \multirow{3}{*}{2009} & With the objective of semantic relation extraction, the authors amalgamated two supervised methods, to be specific, the basic Decision Tree as well as Decision Lists-based Algorithm. They estimated Three semantic relations (i.e., location, social and role) among named entities (NEs). \\ \cline{2-4}  & \multirow{3}{*}{[22]} & On the basis of the dependency graph producing by syntactic analysis, the authors adopted a learning pattern algorithm, denoted \((\mathrm{LP})^{2}\) for generating annotation rules. \\ \cline{2-4}  & \multirow{3}{*}{[19]} & \multirow{3}{*}{2013} & A rule mining approach has been proposed to be applied on an Arabic corpus using lexical, numerical, and semantic features. After the learning features were extracted from the annotated instances, a set of rules were generated automatically by three learning algorithms, namely, Apriori, decision tree algorithm C4.5, and Tertius. \\ \hline \multirow{6}{*}{[9]} & \multirow{3}{*}{2017} & A statistical algorithm was used to extract the simple and complex terms, namely, “the repeated segments algorithm”. For selecting segments that have sufficient weight, the authors used the Weigthing Term Frequency-inverse Document Frequency algorithm (WIT-IDF). Further, a learning approach was proposed based upon the analysis of examples for learning extraction markers to detect new pairs of relations. \\ \hline \multirow{6}{*}{[64]} & \multirow{3}{*}{2018} & Genetic algorithm (GA) was proposed to minimize the computation time needed to search out the informative and appropriate Arabic text features needed for classification. The SVM was used as machine learning algorithm that evaluates the accuracy of the Arabic named entities recognition. \\ \hline \multirow{6}{*}{Hybrid approaches} & \multirow{3}{*}{[65]} & \multirow{3}{*}{2013} & Three methodologies were encompassed: kernel method, co-occurrence, and later rule-based. These methods were utilized for extracting simple and complicated relations regard the biomedical domain. For mapping the data into a feature space of high-dimensionality, Kernel-based algorithms have been used. \\ \cline{2-4}  & \multirow{3}{*}{[23]} & \multirow{3}{*}{2014} & The authors proposed a hybrid rule-based/machine learning approach and a manual technique for extracting semantic relations between pairs on named entities. \\ \cline{2-4}  & \multirow{3}{*}{[4]} & \multirow{3}{*}{2017} & A rules patterns set was defined from compound concepts for inducing of general relations. It utilized a gamification mechanism to specify relations based on prepositions semantics. The Formal Concept Analysis/Relational Concept Analysis approaches were employed for modeling the hierarchical as well as transversal relations of concepts. \\ \hline \hline \end{tabular}
\end{table}