Semantically Enriching the Knowledge Payload of Knowledge Objects Through the Utilization of Knowledge Object Wrappers

Abstract

Aim/Purpose: In this research the authors present the designs of three different knowledge object meta-data wrapper models as a supportive technology to assist the knowledge intensive operations of a network of knowledge, such as a living lab. Background: Within any knowledge driven network environment there is a need to increase the corporate knowledge capacity of the network. The role of experts and knowledge brokers are emphasized, and the exchange of knowledge based on prior experiences informing corporate memories of the members, is the departure point of this research. Methodology: The primary research method applied is that of the design science research methodology supported by experience and application research and the literature. Contribution: Three different metadata models are presented that will when implemented support the informing process within the network of knowledge. The models are grounded on the utilization of metadata elements composing of various key descriptors as found in activity theory and normal means of heuristic enquiry which entail common questions. The elements are annotated and fur-ther enriched using standard JSON-LD IRI pairs. The presented models expand on the extant knowledge of the use of metadata annotations and present a novel way in encapsulating the corporate memories of knowledge workers in the form of knowledge object wrappers. Findings: The results of the evaluation process of the design science research methodolo-gy applied, showed that there is a consensus that the use of knowledge object wrappers as additional metadata, containers could enhance the knowledge ca-pacity and efficiency of a LL and in particular the knowledge brokers.