Abstract:

Retrieval and extraction processes, for enterprise management and decision-making, have gained an excessive importance as the mass of data and information stored in various resources increases. Knowledge is considered a key factor for enterprise prosperity at present and in the future. One of the main problems enterprises face today is the bulk of data derived from various resources. Furthermore, the growth of technology and sciences has greatly influenced the area of management and decision-making procedures, and has dramatically changed the decision-making processes in different levels, both quantitatively and qualitatively. Knowledge management plays a vital role in supporting enterprise learning, since it facilitates the effective collective intellect of the enterprise. Knowledge management is an integrated, systematic process that applies a suitable combination of information technologies and human cooperation in order to identify, manage and share the information capitals. These capitals include databases, documents, policies and procedures. In addition, it both includes the explicit and implicit knowledge of the staff and it applies various and extensive methods to retrieve, store and share knowledge in a certain enterprise.
      Such a question may arise as whether learning and acquiring knowledge can occur in any enterprise structure and framework or we should provide more suitable platforms for it? In response to this question we must say that all enterprises learn, i.e., they adjust to their surrounding environment. However, some of them learn better and more effectively and gain more success in today’s competitive environment. These enterprises are the ones that move towards obtaining the features of a learning organization and forming their enterprise structure on the structures of a learning organization. Even with existing tools and software at hand, there are certain limitations which make the design and implementation of optimal knowledge-based systems for the automatic data inference and query servicing impossible, so that we can take advantage of enterprise knowledge in its best mode in helping and accelerating the managerial affairs of the learning organizations.
        A basic method to turn into a learning organization is applying knowledge management within the enterprise. By facilitating the process of creating and sharing knowledge, and through providing positive working environments and effective rewarding systems, knowledge management accelerates enterprise learning and helps the enterprise adjust itself to today’s rapid changes and hence survive in pace with these changes.
     In this study we aim to show that using ontology and the suitable tools can create a relationship between data settings in various sectors as well as among duties, activities, resources, sectors and information structure of a certain enterprise so that managerial requirements can be desirably met through semantic modeling. As a result, we may have a better chance of using this information for the managers and the users through conceptual queries on the information system of the enterprise. Also, it may be possible to infer from this information for the machine so that we can carry out the decision-making and planning procedures in enterprise processes through automatic inference. Therefore, considering the potentials of ontology in solving such problems and by reference to its features as being simple in structure, capable of sharing, reusing and separating scope knowledge from operational knowledge, knowledge management can improve in learning organizations. Consequently, the following objectives are expected in parallel with the previous works done:
1.    Studying on the potentials of ontology in solving various problems involved in software systems based on knowledge which includes conceptual modeling of the enterprise processes, defining the relationships between tasks, activities and sectors along with their features and limitations.
2.    Classifying significant enterprise information and creating conceptual relationship in databases in order to meet the demands of enterprise management as well as presenting various statistical reports to help intelligent decision-making.
3.    Explaining and presenting methods of creating a database through ontology aiming at explaining the concepts and relationships between the existing enterprise information.
4.    Presenting an ontology-based method to implement conceptual queries and automatic data inferences for the enterprise’s operational knowledge management.
5.    Increasing the machines’ capability in understanding the organizational structure (Intelligent-making) and there for creating the possibility of inferencing and knowledge extracting.
   With this aim, having analyzed the existing concepts in the scope of knowledge management of the learning organizations, we reckon the significance of the information capital of an enterprise and through an ontology-based method we will show the role of this system in improving knowledge management and enterprise and its function in learning organizations.