After the concept of industry cluster [3] was tangibly applied in many countries, companies in the same industry tended to link to each other to maintain their competitiveness in their market and to gain benefits from being a member of the cluster. From the study of ECOTEC in 2005[6] regarding the critical success factors in cluster development, the two critical success factors are *collaboration* in networking partnership and knowledge *creation* for innovative technology in the cluster which are about 78% and 74% of articles mentioned as success criteria accordingly. This knowledge is created through various forms of local interorganizational collaborative interaction [7]. They are collected in the form of tacit and explicit knowledge in experts and institutions within cluster. We applied knowledge engineering techniques to the industry cluster in order to capture and represent the tacit knowledge in the explicit form. 

## 1.2.2 Knowledge Engineering Techniques

Initially knowledge engineering was just a field of the artificial intelligence. It was used to develop knowledge-based systems. Until now, knowledge engineers have developed their principles to improve the process of knowledge acquisition since last decade [8]. These principles are used to apply knowledge engineering in many actual environment issues. Firstly, there are different types of knowledge. This was defined as "know what" and "know how" [9] or "explicit" and "tacit" knowledge from Nonaka's definition [10] Secondly, there are different type of experts and expertise. Thirdly, there are many ways to present knowledge and use of knowledge. Finally, the use of structured method to relate the difference together to perform knowledge oriented activity [11]. 

In our study, many knowledge engineering methods have been compared [12] 
in order to select a suitable method to be applied to solve the problem of industry cluster development; i.e. SPEDE, MOKA, CommonKADS. We adopted CommonKADS methodology because it provides sufficient tools; such as a model suite (figure 1.2) and templates for different knowledge intensive tasks. 

![0_image_0.png](0_image_0.png)

