Philosophy in the Knowledge Structure Pyramid: Knowledge Elicitation and Management

Philosophy in the Knowledge Structure Pyramid: Knowledge Elicitation and Management

Ronald John Lofaro (Embry-Riddle Aeronautical University, USA)
DOI: 10.4018/978-1-7998-2189-2.ch002

Abstract

Currently, and with almost lightspeed, new advances in both human-centered and technology-driven efforts have led to new methods in obtaining, structuring, and using knowledge. This chapter will look at a newer knowledge structuring effort, the knowledge pyramid (KP), attempting to bring in a philosophy-based term, understanding. Knowledge management (KM) has incorporated the philosophy term ontology as a knowledge structuring tool. The chapter will look at how knowledge sets gotten via knowledge elicitation/management can be used and shared to make any organization more effective. The author will consider how the philosophical concept, understanding, can be used in the on-going debate about the structure of KP. The author's goal is to explicate a new model for eliciting, structuring, using, evaluating knowledge for organizational betterment. This resultant paradigm also points the way to future KP debate and research.
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Background

The Delphi method was developed by the now USAF’s Project RAND during the 1950-1960s. and derived its name from a contraction of the term research and development (Research ANd Development). The Delpi technique has long been used for eliciting knowledge from subject matter experts (SMEs) and has become a common methodology/methologies for eliciting analyses, expert opinions and evaluations on a variety of topics. Delphi techniques, a subset of CE/KE, have a goal at arriving at something closer to expert consensus. The Delphi method has been widely adapted to work problems and is still in use today. It has changed over the years from SME anonymity to allowing face-to-face groups (estimate-talk-estimate; ETE) of SMEs. Meister (1985) noted “The (Delphi) methodology is by no means fixed…[it] is still evolving and being researched.” This is as true now as it was when Meister stated it. In point of fact, with the leaps in communication methods and related computer technology, this is even more true today as Delphi techniques have recently begun to look at and attempt to take full advantage of these advances.

One also finds that an expansion in the the field called cognitive/knowledge engineering (CE/KE) has occured and is still in progress. KE was defined in 1983 by Edward Feigenbaum and Pamela McCorduck as follows: “KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.” For a more complete overview and discussion on KE, differing views and uses, the reader is referred to Studer, Benjamins and Fensel (1998).

There is a new emphasis on a related discipline: knowledge management (KM). The field of KE has recently expanded to encompass KM as a subset. KM has been defined as “...the practice of selectively applying knowledge from previous experiences….with the express purpose of improving the organization's effectiveness.” (Jannex, 2014). For works on KM, Amazon's web site has 50 or more, including the ubiquitious The Complete Idiot's Guide to Knowledge Management (Rumizen, 2001).

Pareto Analysis has a long history of being used processes to focus on selecting the best course of action for dealing with issues/problems. Pareto analysis is a formal technique useful where many courses of action are possible. In essence, an estimate or criticality rating is done that allows for selection of effective action (s) that deliver a total benefit to an organization or other entity. Pareto analysis is a creative way of looking at causes/”cures” of organizational problems as it helps stimulate thinking and organize thoughts

Key Terms in this Chapter

United States Federal Aviation Administration (FAA): Is the national aviation authority of the United States. As an agency of the United States Department of Transportation, it has authority to regulate and oversee all aspects of American civil aviation.

Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.

United States Federal Aviation Administration (FAA) Advisory Circulars (AC): The FAA normally published these which are designed to provide assistance and guidelines in complying with FARs; they serve as a “how to” template. The AC’s are titled and numbered and are not legally binding as are the FARs.

Consensus: (For the purpose of this chapter and as used in this chapter) consensus is the state achieved when, if any group participant is asked, alone and outside of the group's hearing, about the consensus/results achieved by the group: their response is that they can support the consensus arrived at, no reservations.

Flight Simulator: A flight simulator (FS) is a system designed to “imitate” the functions of another system (an airplane) in a real operational environment; to be a realistic substitute that responds realistically to flight crew inputs. A FS is a training device whose primary functions are 1.To present information like that which the real system would present, for the purpose of training and, 2. To provide a practice environment that facilitates and enhances the skills and knowledge of the pilot, and thus provides learning which enhances performance in the real system, the airplane. There are several types of flight simulators.

Ontology: A term that comes from philosophy. In KE/KM, an ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse. It is thus a practical application of philosophical ontology, with a taxonomy A KE/KM ontology compartmentalizes the variables needed for some set of computations and establishes the relationships between them; thus, an ontology is used to limit complexity and to organize and structure information.

United States National Transportation Safety Board (NTSB): An independent Federal agency charged by Congress with investigating every civil aviation accident the United States and significant accidents in other modes of transportation – railroad, highway, marine and pipeline. The NTSB determines the probable cause of the accidents and issues safety recommendations aimed at preventing future accidents.

Subject Matter Expert (SME): An individual who exhibits the highest level of expertise in performing a specialized job, task, or skill within the organization.

Facilitator: A person that helps to bring about an outcome (as learning, productivity, or communication) by providing indirect or unobtrusive assistance, guidance, or supervision. At times, the facilitator can provide initial instruction and, at times again, be more proactive as to direction.

United States Federal Aviation Administration (FAA) Regulations (FAR): All are in the Combined Federal Regulations (CFR), Title 14, Aeronautics and Space; each a descriptive title and number; they are legally binding.

Knowledge Pyramid: The DIKW Pyramid, also known variously as the “DIKW Hierarchy”, “Wisdom Hierarchy”, the “Knowledge Hierarchy”, the “Information Hierarchy”, and the “Knowledge Pyramid”, refers loosely to a class of models for representing purported structural and/or functional relationships between data, information, knowledge, and wisdom.

Group dynamics: The interactions that influence the attitudes and behavior of people when they are grouped with others through either choice or accidental circumstances. Social psychologist Kurt Lewin coined the term group dynamics to describe the positive and negative forces within groups of people.

Real Tine Delphi (RTD): An advanced form of the Delphi method. The advanced method “is a consultative process that uses computer technology” to increase efficiency of the Delphi process.

Wisdom: The ability to think and act using knowledge, experience, understanding, common sense and insight. The ability to discern or judge what is true, right, or lasting, insight.

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