# 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  
The comparison to the state-of-the-art was composed of three experiments: (1) comparisons with the other bio-inspired optimization algorithms existing in the literature regarding Arabic ontology learning, (2) comparisons with the previously published approaches on Arabic ontology learning from the text, and (3) comparisons with the state-of-the-art on learning ontology from non-Arabic settings. Firstly, to validate the performance of the proposed G-WOA algorithm in learning ontology from Arabic text, we compared the solution results returned by it to those returned by the ordinary GA and WOA. Moreover, extensive comparisons were conducted by comparing the performance of the G-WOA algorithm to three other bio-inspired algorithms: PSO [44], moth flame optimization (MFO) [45], and the hybrid differential evolution-whale optimization (DE-WOA) [46]. To compare these bio-inspired algorithms, the parameter setting had to be determined for each. Table 4 presents the parameter list used in this work, which was taken from [32; 37; 44; 78]. In each experiment, the tested algorithm was first implemented into one of the previously mentioned corpora. Then, the measures of _PRE_, _REC_, and \(F\) were computed using Equations (31)-(33). This process was repeated for each dataset. Then, the average values of the three measures across all datasets were computed. For each algorithm of the G-WOA, GA, WOA, PSO, MFO, and DE-WOA, we demonstrated the detailed validation results obtained across all the investigated Arabic corpora. The results are demonstrated in Tables A1-A6 of Appendix A, respectively. To sum up, Table 5 presents the total average measures of each algorithm across all the corpora. From Tables 5 and A1, Tables A2-A6, it is apparent that the proposed G-WOA algorithm outperformed the other algorithms in all folds and across all the datasets. The _PRE_, _REC_, and \(F\) results provided by the hybrid G-WOA algorithm were higher when compared to those from the ordinary GA, WOA, PSO, MFO, and the hybrid DE-WOA algorithm. Taking the ACE 2003 (BN) as an example, the results of _PRE_, _REC_, and \(F\) were 98.14%, 99.03%, and 98.59%, respectively. The F-score (_F_) values obtained using the G-WOA were also 98.79%, 98.44%, 98.57%, and 98.63% for ACE 2003 (NW), ACE 2004 (NW), ANERcorp, and the private corpus. Compared to the GA algorithm, the obtained F-score (_F_) values were 93.75%, 93.63%, 93.73%, 93.65%, and 93.41% for the ACE 2003 (BN), ACE 2003 (NW), ACE 2004 (NW), ANERcorp, and the private corpus. On the other hand, the WOA achieved F-score (_F_) values of 96.66%, 96.87%, 96.79%, 96.94%, and 96.81%, respectively for the aforementioned corpora. From these results, the improvement in F-score (_F_) of the ontology learning in comparison to the basic GA algorithm was 4.84%, 5.16%, 4.71%, 4.92%, and 5.22%, respectively, for the same corpora. In comparison to the ordinary WOA, the improvement reached 1.93%, 1.92%, 1.65%, 1.63%, 1.82%, respectively, for the five corpora. These results indicate that the G-WOA was able to accelerate the process of global searching when learning ontology, with its ability to balance effectively both exploration and exploitation. Furthermore, the results also show that the MFO algorithm was more optimal than WOA in ontology learning. This is due to the good ability of MFO to switch between both exploration and exploitation, contrary to the WOA, which was trapped early in local optima throughout the optimization process [37]. Therefore, the MFO algorithm occupies the second rank after the G-WOA in terms of Arabic ontology learning. On the other side, the DE-WOA has a lower performance than G-WOA and MFO across all the datasets. Although the DE algorithm had robust global searchability, it was weak in the exploitation, and converged slowly. Thus, the DE algorithm needs to be optimized for it to be hybridized with other algorithms, as reported in [46]. Thus, the DE-WOA has the third rank in terms of the Arabic ontology learning.