Enhancement of CommonKADS assessment task for domain expert maintainable knowledge-based system / Shamimi A. Halim

Modelling and specifying the knowledge components for future use allow the
Knowledge-based System (KBS) to continuously generate an accurate result and
sustain the competency, efficiency and effectiveness of its problem-solving
capabilities. Knowledge modelling conceptual the knowledge-intensive activities
which include task and domain knowledge while knowledge specification
specifies the inference knowledge that is required to facilitate the KBS reasoning
process. Inference knowledge describes the steps or rules used to perform a task
inference, i.e. making reference to the domain knowledge that is used. This knowledge
is typically acquired by knowledge engineer from the domain experts and
communicated to the system developers during knowledge gathering and system
maintenance phase. Although the involvements of the knowledge engineers and
system developers’ team during the maintenance phase is important, one of the main
issues that remain is the increasing of KBS development cost when both teams are no
longer available to mediate the maintenance task. Hiring new teams, on the other
hand, might cause inconsistency and unreliability of the KBS. Therefore, this
research opts the domain expert to support the extension and reduction of knowledge
within the KBS by focusing on the explicit representation of inference knowledge.
The knowledge within the hospital emergency triage assessment decision making is
selected as a case study. To develop the KBS that able to be maintained by a domain
expert, a European de facto standard for knowledge modelling, the
CommonKADS knowledge engineering methodology is regarded. The generality of
terms and problem-solving methods offered by this methodology nevertheless requires
much effort thus becomes an adaption issue by knowledge engineers and developers.
Hence, this research contributes an adaption guideline and subsequently enhance the
methodology in developing a KBS that enables it to be maintained by the domain
expert. The enhancement was validated by adapting the guideline into two knowledgeintensive
tasks, i.e. classification and diagnosing with two case studies for each
task. The validation attained more than 85% of positive response in adapting the
guideline to model the task and domain knowledge. Next, to validate the ability of the
domain expert to maintain the KBS, the time taken to annotate the inference knowledge
showed that 75% of domain experts able to do annotation with less than five minutes.
The results show that the enhancement provided domain expert with the ability to
maintain a specific Knowledge-based System. As a future work, the user’s experience
should be involved as a part of the validation aspects and the explicit representation of
inference knowledge should be extended into another type knowledge-intensive task.