By establishing a conceptual path through the field of artificial intelligence for objectivistic knowledge artifacts (KAs), the purpose of this paper is to propose an extension to their design principles. The author uses these principles to deploy KAs for knowledge acquired in scientific processes, to determine whether these principles steer the design of KAs that are amenable for both human and computational manipulation.
Adopting the design principles mentioned above, the author describes the deployment of KAs in collaboration with a group of scientists to represent knowledge gained in scientific processes. The author then analyzes the resulting usage data.
Usage data reveal that human scientists could enter scientific KAs within the proposed structure. The scientists were able to create associations among them, search and retrieve KAs, and reuse them in drafts of reports to funding agencies. These results were observed when scientists were motivated by imminent incentives.
Previous work has shown that objectivistic KAs are suitable for representing knowledge in computational processes. The data analyzed in this work show that they are suitable for representing knowledge in processes conducted by humans. The need for imminent incentives to motivate humans to contribute KAs suggests a limitation, which may be attributed to the exclusively objectivistic perspective in their design. The author hence discusses the adoption of situativity principles for a more beneficial implementation of KAs.
The suitability for interaction with both human and computational processes makes objectivistic KAs candidates for use as metadata to intersect humans and computers, particularly for scientific processes. The author found no previous work implementing objectivistic KAs for scientific knowledge.
