Capturing, Annotating and Processing Practical Knowledge by Using Decision Trees

Practical knowledge describes the knowledge resulted from experience of a person. Capturing and processing practical knowledge is usually difficult, because it is only available in a persons head. However, this knowledge would help inexperienced persons to obtain practical knowledge in a fast way. Approaches so far have only considered how already formalized knowledge can be shared and integrated across different systems. However, capturing and formalizing practical knowledge and working around with incomplete data have not yet been considered. To address this problem we 1.) developed an open-source extension for Semantic MediaWiki that supports the graphical modeling of practical knowledge; 2.) enable to enrich the formalized practical knowledge with semantics from ontologies and knowledge graphs with references to external data sources and rules and 3.) present a technical infrastructure to automatically execute the created decision trees and thus to retrieve recommendations of the practical knowledge in real-time.