Co-evolution of Knowledge Bases: Languages and Tools

Useful domain knowledge resides in the heads of key employees, but it remains to a large extent tacit. Storing this knowledge explicitly in a so-called knowledge base (KB) has many advantages. However, the process of building a KB by having a knowledge engineer repeatedly interview the domain experts, is slow and costly. To alleviate this, we envision a co-evolution process where the domain expert and knowledge engineer co-create the KB together, with the additional advantage that the domain expert “retains ownership” of the KB. To facilitate co-evolution, a formal language that is both understandable for domain experts and expressive enough to represent complex knowledge is needed. In this project, we will investigate 3 potential formalisms to facilitate co-evolution: cDMN (an extension to DMN), abstract syntax trees and controlled natural language. After the KB is co-created, it should be maintained, preferably by the domain experts themselves. For this task, we will investigate verification techniques for the proposed formalisms, test case generation techniques to validate the KB, and the use of inductive logic programming to automatize refinement of the knowledge. This project will develop and evaluate the proposed techniques in the context of five case studies currently executed by the research group, covering a variety of domains. If successful, this will enable domain experts to co-create the KB, perform the maintenance task, and reclaim ownership of their knowledge.