Knowledge engineers elicit the knowledge of experts primarily through detailed interview sessions with the subject-matter experts. This process depends on the expert's personality and the Knowledge engineer's experience and preparation. "Because of its hierarchical and modular nature, context-based reasoning lends itself well to automating the knowledge capture process" (Becerra-Fernandez and Sabherwal, 2010, pp.140-141).

The example of the knowledge capture system can be Context-based Intelligent Tactical Knowledge Acquisition (CITKA). The CITKA system has been developed to facilitate the acquisition of the knowledge for military tactics. The system is based on the contextbased modeling paradigm called Context-based Reasoning (CxBR), which has a highly structured form and hierarchical organization that lends itself well to an automated query system, we believe this approach can reduce the effort required to build the models, as well as reduce errors. CxBR is based on the idea that in executing a mission, an gent will experience several different situations, all in equence. Each situation will require certain skills and actions in order to successfully navigate/survive it (Castro, Gonzalez and Gerber, 2002).

CITKA consists of four modules of independent subsystems:

• Knowledge engineering database back-end (KEDB)
• Knowledge engineering interface (KEI)
• Query rule-base back-end (QRB)
• Subject matter expert interface (SMEI) (Becerra-Fernandez and Sabherwal, 2010, p.141).

You can get an overview of the system in the article written by Castro, Gonzalez and Gerber (2002) http://www.mind.foi.se/SAWMAS/SAWMAS-2002/Papers/SAWMAS-02-JCastro.pdf