Objectivistic knowledge artifacts
Rosina O. Weber
Department of Information Science, Drexel University, Philadelphia,
Purpose –By establishing a conceptual path through the field of artificial intelligence for objectivistic
knowledge artifacts (KAs), the purposeof this paper is to propose an extensionto their design principles. The
author usesthese principles to deployKAs for knowledge acquiredin scientific processes, todetermine whether
these principlessteer the design of KAs that are amenable forboth human and computational manipulation.
Design/methodology/approach –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.
Findings –Usage data reveal that huma n scientists could enter sc ientific KAs within the pro posed
structure. The scien tists were able to create associati ons among them, search and retrieve KAs, and reuse
them in drafts of reports to f unding agencies. These results were ob served when scientists were motivat ed
by imminent incentives.
Research limitations/implications –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.
Originality/value –The suitability for interaction with both human and computational processes makes
objectivisticKAs candidates for use as metadatato intersect humans and computers,particularly for scientific
processes. The authorfound no previous work implementingobjectivistic KAs for scientific knowledge.
Keywords Knowledge management, Scientific knowledge, Artificial intelligence, Lessons-learned systems,
Objectivistic knowledge artifacts, Scientific knowledge artifacts
Paper type Research paper
Knowledge and its artifacts are of interest to both social and computer sciences. Cabitza and
Locoro (2014) propose a conceptual framework for both perspectives, which they refer to as
situativity and objectivity. Cabitza and Locoro (2014), along with many others (e.g. Simone,
2015; Cabitza et al., 2013), describedthe socially situatedstance in detail. Further description of
the objectivity stance is still needed, particularly given the increasing ubiquity of
computational representations of socially motivated knowledge cycles. In the field of science
alone,where human activitiesare considered a bottleneckto scientific progress(Gil et al., 2014),
entire scientific steps including hypothesesgeneration are being automated( Bohannon, 2017).
This reality makes it urgent that both situativity and objectivity stances coexist to design
systems that guarantee that humans are kept in the loop and can understand what the
automated methods implement and the results they obtain.
This paper’s intended contribution is to extend previous work describing the
objectivistic stance through concepts conceived in the fields of artificial intelligence (AI) and
knowledge engineering (KE), and to propose its design principles, which are covered in
Section 2. In order to provide guidance to others attempting to utilize and develop such
Data Technologies and
Vol. 52 No. 1, 2018
© Emerald PublishingLimited
Received 1 March 2017
Revised 4 September 2017
Accepted 5 September 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
This work was supported by the US EPA-Science to Achieve Results (STAR) Program and the US
Department of Homeland Security Programs, Grant No. R83236201. The author thanks the members of
the CAMRA community, the invited editors, and the reviewers who helped improve this article. Special
thanks also to Adam J. Johs for his comments. The work described in the retrieval experiment was
conducted in the period from 2005 to 2011 under IRB Protocol No. 16449.