# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 5 Experimental Results
### Discussion
#### 5.4.1 Comparison to the State-of-the-Art
In contrast, the convergence speed is a crucial criterion for evaluating the performance of any optimization method. Therefore, the convergence time for the proposed hybrid G-WOA algorithm was computed and compared to the time obtained by all algorithms versus the false alarms rate. The false alarms rate was computed in this paper using Equation (22). As depicted in Figures 2 and 1 (of \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**GA** & **WOA** & **PSO** & **MFO** & **GA-WOA** & **DE-WOA** \\ \hline Population size: 100 & Population size: 100 & Particles number \(P\): 10 & Population size: 100 & Population size: 100 & Population size: 100 \\ \hline Maximum generations: 500 & The random variable \(r\) : \([-1,\ 1]\) & Iterations number \(t\): 10 & The constant defines the logarithmic spiral shape \(h\): 10 & Maximum generations: 500 & Scaling factor for DE: Random between 0.2 and 0.8 \\ \hline Crossover probability \(Cr.\) & Logarithmic spiral shape \(h\): 1 & Acceleration \(c1\): 2 & The random variable \(r\) : \([-1,\ 1]\) & Crossover probability \(Cr.\) 0.9 & DE mutation scheme: \(\text{DE/best/1/bin}\) \\ \hline Mutation rate \(MR\): 0.05 & \(e\): Decreased from 2 to 0 & Acceleration \(c2\): 2 & Mutation rate \(MR\): 0.05 & The random variable \(r\) : \([-1,\ 1]\) \\ \hline Reproduction ratio: 0.18 & Maximal weight of inertia: 0.7 & Maximal iterations number: 10 & Logarithmic spiral shape \(h\): 1 \\ \hline Selection: weighted Roulette Wheel & Minimal weight of inertia: 0.1 & The random variable \(r\) : \([-1,\ 1]\) & \(e\): Decreased from 2 to 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The parameter list used in this work. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Average Measures**} \\ \cline{2-5}  & _PRE_ (\%) & _REC_ (\%) & \(F\) (\%) \\ \hline Proposed GA-WOA & 98.48 & 98.73 & 98.6 \\ GA & 93.71 & 93.56 & 93.63 \\ WOA & 96.31 & 97.34 & 96.81 \\ PSO & 95.04 & 94.95 & 94.99 \\ MFO & 97.73 & 96.96 & 97.34 \\ DE-WOA & 97.07 & 95.93 & 96.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average measures for each algorithm across all datasets (detailed results of algorithms can be followed in Tables 1-6 of the Appendix A). Appendix A), when following the WOA algorithm across all the Arabic datasets, we see that it took a lower convergence speed in comparison to the proposed hybrid G-WOA algorithm. This can be interpreted by the poor exploitation ability for the ordinary WOA algorithm, which requires a long time to search for the offspring and parents. On the contrary, the hybrid G-WOA algorithm overcame this drawback by combining the genetic operations into the WOA algorithm. Secondly, to investigate the efficacy of the proposed approach that integrates the text mining and G-WOA algorithms to learn ontology from the Arabic text, we performed a comparison between it and the more recent works that use the same Arabic corpora, in terms of precision (_PRE_), recall (_REC_), and F-score (_F_). Table 6 shows the comparison. Compared to the other methods presented in the literature, as Table 6 shows, the proposed approach yielded superior results in terms of _PRE_, _REC_, and \(F\) measures. These results demonstrate the robustness of integrating text mining and G-WOA algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Corpus** & **Approach** & **Accuracy** \\ \hline \multirow{4}{*}{[79]} & \multirow{4}{*}{2019} & \multirow{4}{*}{2019} & ACE 2003 (NW), ACE 2003 (BN), ACE 2004 (NW), and ANERcorp. & The Long-Short-Term-Memory neural tagging model was augmented with the Convolutional Neural Network to extract the character-level features. & \(F=91.2\%\), \(94.12\%\), \(91.47\%\), and \(88.77\%\), respectively, for the four corpora. & \(F=91.2\%\), \(91.47\%\), and \(88.77\%\), respectively, for the four corpora. & \(F=83.5\%\), \(88.77\%\), respectively, for the four corpora.