# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 5 Experimental Results
### Discussion
#### 5.4.2 Contributions to the Literature  
From the previous results, the proposed approach outperformed the state-of-the-art approaches to Arabic ontology learning. Likewise, it was also noted that very little research has used the evolutionary approaches, whereas no hybrid bio-inspired algorithms were investigated by the previously published works. The majority of studies have depended on the hybridization of rule-based and machine learning approaches [4; 23], which have shortcomings, as previously discussed in the introduction section. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Corpus** & **Approach** & **Accuracy** \\ \hline \multirow{4}{*}{[83]} & \multirow{4}{*}{2019} & \multirow{4}{*}{LLL and IEPA} & \multicolumn{3}{c}{A logic-based relational learning method} \\  & & & for Relation Extraction utilizing the & \multirow{2}{*}{\(F=79.9\%\) and 76.1\%, respectively,} \\  & & & Inductive Logic Programming, namely & \\  & & & OntoILPER, to generate symbolic & \\  & & & extraction rules. & \\ \hline \multirow{4}{*}{[84]} & \multirow{4}{*}{2015} & \multirow{4}{*}{LLL and IEPA} & \multicolumn{3}{c}{An optimized tree kernel-based} \\  & & & _Protein-protein_ extraction approach. The & \\  & & & modal verbs together with the appositive & \\  & & & dependency features were used for & \\  & & & defining some relevant rules that expand & \\  & & & and optimize the shortest dependency & \\  & & & path in between two proteins. & \\ \hline \multirow{4}{*}{[85]} & \multirow{4}{*}{2015} & \multirow{4}{*}{LLL and IEPA} & \multicolumn{3}{c}{Three word representational techniques} \\  & & & including vector clustering, distributed & \\  & & & representation, and Brown clusters. The & \\  & & & SVM was used for unsupervised learning. & \\ \hline \multirow{4}{*}{[86]} & \multirow{4}{*}{2012} & \multirow{4}{*}{LLL and IEPA} & \multicolumn{3}{c}{Tree kernel-based extraction approach, in} \\  & & & which the tree representation produced & \\ \cline{1-1}  & & & from constituent syntactic parser, was & \\ \cline{1-1}  & & & refined utilizing the shortest dependency & \\ \cline{1-1}  & & & route in between two proteins estimated & \\ \cline{1-1}  & & & from the dependency parser. & \\ \hline Proposed & \multirow{2}{*}{2019} & \multirow{2}{*}{LLL and IEPA} & \multicolumn{3}{c}{Integrating text mining and G-WOA} \\  & & & algorithms. & \\ \cline{1-1}  & & & & the two corpora. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison to the state-of-the-art in non-Arabic settings. Figure 2: The convergence time versus the FAR rate for all algorithms using the ACE 2003 (BN) corpus. Some works used deep learning algorithms [78; 79] like the long-short-term-memory and convolutional neural network, but the results are still below expectation. In [78], a deep neural network-based method was proposed. The application results to the ANERcorp were 95.76%, 82.52%, and 88.64%, in terms of _PRE_, _REC_, and \(F\) measures. These results are also lower than those obtained using the approach proposed in this work. In [79], the \(F\) measure results obtained using the presented deep learning approach were 91.2%, 94.12%, 91.47%, and 88.77%, respectively, for the ACE 2003 (NW), ACE 2003 (BN), ACE 2004 (NW), and ANERcorp. These results are also lower than those obtained by applying the approach proposed in this work to the same corpora, which reveals the efficiency of our approach. The results presented in [78; 79] reveal the need for enhancing the performance of the deep learning methods and to overcome their shortcomings such as being stuck in the local optima, when applied to the natural language processing, for instance, through using the bio-inspired optimization algorithms. The proposed ontology learning approach is also applicable with non-Arabic texts. Furthermore, the comparisons to the state-of-the-art approaches on learning ontology using the same non-Arabic corpora demonstrate higher results in favor of the proposed approach.