# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 6 Conclusions  
The majority of the state-of-the-art works on Arabic ontology learning from texts have depended on the hybridization of the handcrafted rules and machine learning algorithms. Contrary to the literature, this study presented a novel approach for Arabic ontology learning from texts which advances the state-of-the-art in two ways. First, a text mining algorithm was proposed for extracting the initial concept set from the text documents together with their semantic relations. Secondly, a hybrid G-WOA was proposed to optimize the ontology learning from Arabic text. The G-WOA integrates the genetic search operators like mutation, crossover, and selection into the WOA algorithm to achieve the equilibrium between both exploration and exploitation, in order to find the best solutions that exhibit the highest fitness. The experimental results revealed the following conclusions.  
Firstly, as for learning Arabic ontology from texts, the proposed GA-WOA outperformed the ordinary GA, and WOA across all the Arabic datasets in terms of _PRE_, _REC_, and \(F\) measures. When comparing the solution results obtained using the G-WOA to those obtained using the ordinary GA, we found an improvement in F-score (_F_) by up to 4.84%, 5.16%, 4.71%, 4.92%, and 5.22%, respectively, for ACE 2003 (NW), ACE 2004 (NW), ANERcorp, and the private corpus. Furthermore, the improvement reached 1.93%, 1.92%, 1.65%, 1.63%, 1.82%, respectively for the same corpora when using the ordinary WOA algorithm. Secondly, the G-WOA also outperformed the PSO, DE-WOA, and MFO across all the Arabic corpora, in terms of the three measures. The MFO occupies the second rank after the G-WOA, in terms of ontology learning from Arabic text. This was interpreted by the good ability of MFO to switch between both exploration and exploitation. Thirdly, the G-WOAoutperformed the other algorithms in convergence speed. Taking the WOA as an example, it is found to have low convergence due to its poor exploitation. Thus, the G-WOA algorithm is superior when compared to the other bio-inspired algorithms in terms of convergence speed.  
Furthermore, the G-WOA exhibited low rates of false alarms across all the Arabic datasets, in comparison to the other algorithms. Fourthly, the proposed Arabic ontology learning approach, which is based on the synergy of text mining and G-WOA algorithms, outperformed the state-of-the-art in terms of precision (_PRE_), recall (_REC_), and F-score (_F_). This was due to its high capability to extract the concepts along with the semantic relations from the Arabic documents, then creating a population of search agents (solutions) that include genes represent the initial concepts. Moreover, the G-WOA starts to search for the best solution through a set of iterations, including embedding the genetic operators into the WOA architecture. Eventually, the algorithm returns the solution which recommends the best set of concepts/relations that can contribute to the ontology. Eventually, the proposed ontology learning approach is applicable to the non-Arabic texts. It achieved higher performance that outperformed the state-of-the-art approaches on learning ontology from the non-Arabic text.