# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 1 Introduction
The drawbacks of the rule-based methods include being time-consuming and having the requirement to fully cover all rules which may represent any kind of relationship. The works that have proposed machine-learning approaches for ontology learning are based on conventional classification algorithms that categorize Arabic relations into corresponding types, but do not provide any solutions to overcome the drawbacks of these classification algorithms, such as their low performance when analyzing large textual datasets and high-dimension data. Some works have attempted to overcome the shortcomings of the two previous methods by integrating inference rules and machine learning algorithms into hybrid approaches. Although this hybridizationhas somewhat optimized the overall performance, more advanced hybrid approaches to optimize Arabic ontology learning are still required. By comparison to the other languages, several studies have been conducted for learning ontology from English text [24; 25; 26], which has achieved the largest number of contributions among the other languages. Some of these studies presented rule-based approaches [24], and the others proposed machine-learning-based approaches [25; 26]. In [24], the authors presented a rule-based approach for learning the English ontology in which the inductive logic programming was used to obtain ontology mapping. This method described the ontology in OWL format and then interpreted it into first-order logic. Thereafter, it generated generalized logical rules depending on background knowledge, just as mappings do. In [25], an exemplar-based algorithm was introduced to link the text to semantically similar classes in an ontology built for the domain of chronic pain medicine. In [26], a machine learning approach based upon a neural network was presented to learn ontology through the encoder-decoder configuration. Accordingly, the natural language definitions were translated into Description Logics formulae through syntactic transformation. These methods of building ontologies are domain-specific. Therefore, they are not applicable with the Arabic language and do not support the Arabic texts. Recently, the hybrid approaches of different bio-inspired optimization algorithms [27; 28; 29] demonstrated competitive performances in different applications of computer science, where two or more algorithms of the following are used as hybrid to optimize the problem in the domain of interest: the genetic algorithm (GA) [30; 31; 32], social spider optimization [33; 34], ant colony optimization (ACO) [35; 36], and whale optimization algorithm (WOA) [37; 38]. These methods have several merits, including having a small parameter set, simple frameworks, and capability to avoid the shortcoming of being trapped in the local optima. Thus, they are suitable for several real applications and have the robustness to solve many problems of global optimization without the need to change the original algorithm structure. In between these algorithms, the WOA was introduced in [39] for solving the global optimization problem through emulating the humpback whales behavior. These humpback whales are well known of a hunting method, namely, bubble-net feeding [39]. This behavior operates in three phases, including coral loop, lobtail, and capture loop [39]. The extra information on this behavior can be found in [40]. In comparison to the other bio-inspired optimization algorithms, such as Particle Swarm Optimization (PSO), the WOA algorithm has a good exploration capability of the search space [37]. However, it suffers from poor exploitation and the probability to be trapped into local optima. In addition, GA is another heuristic algorithm for combinatorial optimization [31]. In comparison to the other similar algorithms like Tabu Search (TS) [41; 42] and simulated annealing (SA) [43], we can find that all of them are applied for several combinatorial optimization problems. Furthermore, they also have different properties. First, a great computational cost is required by GA to find the optimal solution.