Biological technology (BT) is growing rapidly and providing a wide range of applications in our society today. It is considered one of the most research-intensive and knowledge-dependent industries. However, heavy upfront capital investment and long product development period make it an extremely high risk industry and time to market plays a critical role in BT R＆D projects.
Essentially, the driving force underneath those developments and processes are research laboratories of BT firms, universities and other research institutions. Meanwhile, knowledge management practices are not yet popularly applied in BT laboratories. This problem is the result of fast pacing industry, researchers' frequent job switching, relatively low awareness of utilizing contemporary knowledge management concepts and tools.
BT laboratories produce a large amount of data, especially experimental data, and store them in LIMS (Laboratory Information Management System). While well-established knowledge is provided in firm of text, experiment protocols, and research papers, information in LIMS can be considered as a source for BT implicit knowledge.
This paper adopts and customizes knowledge map, RBR (rule-based reasoning) and CBR (case-based reasoning) techniques for LIMS and KMS (knowledge management system) integration. The integrated system aims at assisting BT researchers in experiment design and troubleshooting. By doing so the expected result is to increase knowledge utilization and productivity in BT laboratory environment.
In our model, BT experiment knowledge is structured and stored into knowledge maps, which represent the relationships between research object characteristics and experiment's component settings. The details of those relationships are stored in rule library, which consists of both quantitative and qualitative rules. This knowledge map and rule library is the base on which RBR and CBR technique will perform appropriate tasks to utilize both implicit and explicit knowledge.
The more the knowledge map, rules library and historical experiment case library are built up, the more knowledge is articulated and the more precise suggestions are provided by the system. As a final result, it enables BT laboratory researchers work more productively and effectively.
