AuthorSheridan, William Patterson
Local subject classificationknowledge work
personal knowledge management
MetadataShow full item record
CitationHOW TO THINK LIKE A KNOWLEDGE WORKER 2008,
DescriptionA guide to the mindset needed to perform competent knowledge work.
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Nine Principles of Knowledge Organization. Preprint of paper published in: Advances in Knowledge Organization, 1994, Vol. 4, pp 91-100. (Proceedings of the Third International ISKO Conference 20-24 June 1994 Copenhagen, Denmark).Hjørland, Birger (1994)The core problem in Information Science (IS) is in my opinion information seeking and "information retrieval", (IR), which is aimed at helping users become informed by helping them identify documents, which are the "best textual means to some end" (Wilson, 1968). Other problems, such as the design of information systems and knowledge organization (e.g. by classification and indexing) should be seen as means to that end. However, IS has ignored some fundamental problems, which questions the possibility of having a profession and a discipline trying to solve the above mentioned problems. Much research in IS has been based on certain problematic views of knowledge, and searched for principles of knowledge organization, which are independent of claims of subject-knowledge. In this paper, we shall look at the problems of knowledge organization based on a view of knowledge as a historical developed product in which principles of organization is tied to domain-specific criteria. The article is organized as an argumentation for nine principles on the organization of knowledge: Principle # 1: Naive-realistic perception of knowledge structures is not possible in more advanced sciences. The deepest principles on the organization on knowledge rest upon principles developed in and by scientific disciplines. Principle # 2: Categorizations and classifications should unite related subjects and separate unrelated subjects. In naive realism, subject relationships are based on similarity. Two things or subjects are seen as related if they are "alike", that is if they have common properties (descriptive terms) ascribed. Principle # 3 For practical purposes, knowledge can be organized in different ways, and with different levels of ambition: Principle # 4: Any given categorization should reflect the purpose of that categorization. It is very important to teach the student to find out the lie of the land and apply ad hoc classifications, pragmatic classifications or scientific classifications when each kind of classification is most appropriate. Principle # 5: Concrete scientific categorizations and classifications can always be questioned. Principle # 6: The concept of "polyrepresentation" (cf. Ingwersen, 1994) is important. Principle # 7: To a certain degree different arts and sciences could be understood as different ways of organizing the same phenomena. Principle # 8: The nature of disciplines varies. Principle # 9: The quality of the knowledge production in many disciplines is in great trouble
Facilitating knowledge sharing in organizations: Semiautonomous agents that learn to gather, classify, and distribute environmental scanning knowledge.Nunamaker, J. F.; Elofson, Gregg Steven.; Sheng, Olivia; Chang, Yih-Long; Daniel, Terry; Garrett, Miller (The University of Arizona., 1989)Evaluating patterns of indicators is often the first step an organization takes in scanning the environment. Not surprisingly, the experts that evaluate these patterns are not equally adept across all disciplines. While one expert is particularly skilled at recognizing the potential for political turmoil in a foreign nation, another is best at recognizing how Japanese government de-regulation is meant to complement the development of some new product. Moreover, the experts often benefit from one another's skills and knowledge in assessing activity in the environment external to the organization. One problem in this process occurs when the expert is unavailable and can't share his knowledge. And, addressing the problem of knowledge sharing, of distributing expertise, is the focus of this dissertation. A technical approach is adapted in this effort--an architecture and a prototype are described that provide the capability of capturing, organizing, and delivering the knowledge used by experts in classifying patterns of qualitative indicators about the business environment. Using a combination of artificial intelligence and machine learning techniques, a collection of objects termed "Apprentices" are employed to do the work of gathering, classifying, and distributing the expertise of knowledge workers in environmental scanning. Furthermore, an archival case study is provided to illustrate the operations of an Apprentice using "real world" data.