Ontology based Approach in Knowledge Sharing Measurement

For many years, physical asset indicators were the main evidence of an organization’s successful performance. However, the situation has changed following the revolution of information technology in the knowledge-based economy and in the new ideas in economy; knowledge assets are a critical strategic resource in economy. Knowledge management [KM] tools have become very important and in order to gain a competitive advantage, it is necessary to create, store, share and apply knowledge. Knowledge sharing is one of the key issues in knowledge management. One of the main challenges facing pioneer firms is to provide an effective strategy to exchange knowledge formally or informally. In this paper, we will discuss the effectiveness of knowledge sharing and our proposal for an effective knowledge sharing strategy. Based on a review of knowledge sharing literature, we will focus more on the trust and knowledge contexts as key issues in knowledge sharing. Trust is the most important issue when creating a relationship, knowledge sharing and partnership. Moreover, there are a number of forms that trust can take in these relationships and the most regularly cited forms are competence and benevolence trust. In this paper, we will explore these two forms of trust and will examine their role in knowledge sharing and how they can be defined and measured. On the other hand, we will apply ontologies to explore the knowledge context. Ontologies are used in widespread application areas particularly to provide a semantically shared domain knowledge in a declarative formalism for intelligent reasoning. Even ontology enables knowledge sharing; however, the complexity of knowledge being conceptualized in the ontology is critical to the success of knowledge sharing efforts. Other factors like trust in the source of knowledge can also affect knowledge transfer. In this paper, we propose metrics to measure the complexity of ontology for knowledge sharing. Finally, the effectiveness of our proposed knowledge sharing methodology is presented both using a fuzzy-inference engine and a crisp system.