## 2 Literature Review
### Background Work
#### 2.1.2 Knowledge Representation  
An ontology consists of classified concepts and relations related to domain knowledge [33]. In the knowledge representation phase, metadata are retrieved from the acquired knowledge sources, and knowledge objects are marked in terms of the domain ontology. The marked objects can also be stored in the local language or in multiple languages. The  
Figure 1: Ontology-based structured knowledge management system. marked objects are placed in the knowledge base, which is promptly used for the effective search of knowledge objects. There are variety of tools and techniques used for knowledge mining, but knowledge representation is a key consideration for all of them. A knowledge representation scheme must be selected to obtained a well-established description of a domain [14; 15; 16]. Decision Trees (Semantic Nets)  
When using decision trees, knowledge is represented in graphs or hierarchical formats [34]. In such graphs, concepts are represented by nodes or vertices and the relations between these nodes (concepts) are represented by edges or lines. Conditional Rules (Symbolic Rules)  
IF-THEN conditional rules can be applied to represent domain knowledge. One or more logical operators such as \"AND\", \"OR\", and \"NOT\" are used to connect conditions in this type of technique [34]. Fuzzy Logic (Fuzzy Rules)  
Fuzzy types of logic or rules can well-represent vague concepts in terms of \"high\" or \"low\". Fuzzy logic sets membership criteria by using a membership function in a range from 0.0 to 1.0, where these values represent absolutely false or absolutely true, respectively [35]. Case Based (4 Rs Strategy)  
Case-based techniques maintain the history of previous cases and their respective solutions. They can be used to find and implement a previous solution to solve similar cases, which they achieve through the implementation of the \"retrieve\", \"reuse\", \"revise\", and \"retain\" strategy [36]. Neural Networks (Connectionist)  
The unique neural network approach consists of various processing units known as neurons connected to each other. These can efficiently produce conclusions, and this inference depends on numerical computations. A major drawback of neural networks is that they lack a natural representation of knowledge. Therefore, the knowledge produced by neural networks is not always comprehensible [37]. The merge or integration of two or more schemes can also solve domain knowledge representation problems in structured knowledge management. The authors of [17; 31] argued that the best scheme (compared with single or integrated approaches) is one that satisfies the user requirements for a knowledge management system. In the knowledge connection phase, the knowledge is regulated, added, updated, edited, and saved by the managers of the knowledge base. This enables a system to competently and dynamically participate in confined usage and maintenance. Thus, users are able to find related data in different ways according to their requirements and preferences. Related Work  
An ontology construction scheme provides a developer's guidelines, processes, and techniques for ontology construction [38]. A variety of ontology construction schemes have been proposed in the literature [14; 17; 18; 19; 20; 30; 39; 40]. It is important to notice that different authors have adapted domain ontology construction methodologies in different ways; therefore, there is no common or universal methodology for ontology construction [21]. Furthermore, different ontology construction methodologies focus on different aspects of domains in the process of ontology construction. For instance, some focus on the scope of the ontology and the analysis of the domain, while others focus on the validation of the ontology. In addition, we cannot find details regarding the performed activities and design descriptions for the ontology construction methodologies presented in the literature.