# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 1 Introduction  
In recent times, the internet has become people's principle source of information. A huge quantity of web pages and databases is accessed every day. The instant growth in the quantity of information accessed via the Internet has caused difficulty and frustration for those trying to find a particular piece of information. Likewise, the various kinds of information resources that exist on the Internet constitute an enormous quantity of information in the form of web pages, e-libraries, blogs, e-mails, e-documents, and news articles, all containing huge amounts of data [1]. Such information is unstructured or semi-structured, which means that the knowledge discovery process is challenging. To deal with this challenge, the semantic web was invented as an extension of the ordinary web [2]. Ontology is a method for extending web syntactic interoperability to semantic interoperability. Ontologies are exploited to represent huge data in such a way that allows machines to interpret its meaning, allowing it to be reused and shared [3]. They are formal and explicit specifications of concepts and relations [4] and play a crucial role in improving natural language processing (NLP) task performance, such as information extraction and information retrieval. Ontologies are usually restricted to a particular domain of interest. The preliminary identification of ontology is expressed as Characterization of Conceptualization. The ontology learning from texts is \"The acquisition of a domain model from textual corpus\" [5]. Building ontologies can be accomplished manually, automatically, or in a semi-automatic way. However, the manual building of ontologies has the drawbacks of being time-consuming, expensive, and error-prone [6]. Furthermore, it demands the cooperation of ontology engineers and domain experts. In order to avoid these shortcomings, ontology learning has evolved to automate or semi-automate the construction of ontologies. Ontology learning includes knowledge extraction through two principle tasks: concepts extraction (which constitute the ontology) and extracting the semantic relations that link them [7; 8; 9]. Despite the Arabic language's importance as the sixth most spoken language in the world [2] and the tremendous growth of Arabic content via the web in recent years, it has been given little attention in the ontology learning field [10; 11; 12]. Several contributions are available on domain ontologies in English [13; 14; 15] and other languages. However, Arabic is not commonly considered by specialists in this field. Furthermore, the automatic extraction of semantic relationships from Arabic corpora has not been extensively investigated in comparison to other languages such as English. The majority of attempts to construct Arabic ontology is still implemented manually [2; 16]. Manually developing conceptual ontologies is not only a time-consuming but also a labor-intensive job. Furthermore, extra challenges are encountered when extracting knowledge from Arabic texts due to the nature of the Arabic language, the words' semantic vagueness, and the lack of tools and resources which support Arabic. Consequently, the Arabic language suffers from a lack of ontologies and applications in the semantic web [17; 18]. In summary, only a few studies have considered automatic ontology learning from Arabic text [4; 9; 12; 19; 20; 21; 22; 23]. These works fall into one of the following three categories: handcrafted rule-based methods [12; 20; 21], machine learning methods [9; 19; 20; 22], and hybrid rule-based/machine learning methods [4; 23]. The studies that have introduced rule-based approaches for ontology learning are based on extracting the semantic relationships between Arabic concepts or Arabic named entities, and utilize the same technique, which can identify linguistic patterns from a given corpus. These patterns are then converted to rules and transducers.