## 1 Introduction  
The current era is the age of information technology and the implementation of new and advanced ideas in organizations; therefore, the large and varied amount of implicit, explicit, structured, and unstructured knowledge is increasing every day. Various knowledge management techniques have been introduced to effectively collect and manage such complex and varied knowledge. Typically, a knowledge management system is developed as a framework based on computational intelligence and used to share intellectual information. Recent research has shown that various companies and businesses invest funds to implement knowledge management systems to increase the efficiency of their organization operations or processes [1]. A structured knowledge management system involves multidisciplinary groups of users who resolve common issues at the domain level [2, 3]. In this system, a domain provides well-organized knowledge for the management of and robust coordination between various autonomous departments. Typical examples of structured knowledge management are waste management systems, postal codes, the Semantic Web, flood management systems, map preparation, the Olympic Games, and geographic information management systems. Recently, research on these systems has focused on the integration of ontologies in knowledge management. However, the conceptualization and integration of ontologies should be based on organizational facts [4]. Ontology-supported knowledge systems enable a system and user to exchange knowledge-based information with each other due to the common knowledge of a specific field or domain. Ontology is the most essential and primary component of knowledge-based applications. Ontologies are required in knowledge-based systems to serve as domain descriptions (i.e., formal models) that are machine-understandable. Knowledge management systems depend on ontologies to transform data into a machine-readable form to ensure reliable performance. Various ontologies have been introduced for general use, e.g., WordNet and Cyc [5], but most applications need ontologies related to some specific domains to define the relevant concepts and relations. As organizational knowledge is distributed in nature, knowledge-based systems need to integrate knowledge from different sources. This integration can only be achieved through the use of ontologies [6]. However, finding ontologies for this purpose is a big problem [1]. Ontology construction has been an important research domain in recent years [7; 8; 9; 10; 11; 12]. Governments and industries have adopted ontology-based knowledge management systems and developed innovative applications in the deployment of semantic technology. A variety of ontology construction schemes have been presented by various scholars [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Studies of ontology construction schemes have mostly discussed the conceptualization, evaluation, domain analysis, implementation, and instantiation aspects (including domain ontology), but few of them have provided details about the adopted schemes [29]. Therefore, it is difficult to understand the schemes for the construction of ontologies for specific domains despite the need for well-designed schemes to perform the complex, time-consuming, and tedious task of ontology construction. An ontology construction method must comprise a detailed and clear discussion of the methodology, processes involved, and guidelines for the developers [30]. In this research, a scheme for ecological and confined domain ontology construction (EC-DOC) in structured knowledge management is proposed based on the ontology construction schemes of [6; 31; 32]. The proposed scheme provides a general structure for domain ontology construction that can be implemented in the development of any domain ontology-based structured knowledge management system.