Knowledge Engineering Methodology with Examples

Knowledge Engineering Methodology with Examples

Ronald John Lofaro (Embry-Riddle Aeronautical University Worldwide, USA)
DOI: 10.4018/978-1-4666-5888-2.ch451
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Background

Knowledge Engineering

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.” Some of the possible uses and functions of KE are: articulation and assessment of an issue/problem; development of a knowledge-based system structure for dealing with issues/problems; obtaining and structuring relevant information and knowledge; developing tests for validation of the obtained information/knowledge...and more. Since the mid-1980's, KF has grown in use and importance concomitant with the advances in computer memory, capabilities and useage. Knowledge engineering is also linked to cognitive science and socio-cognitive engineering where the knowledge is produced by socio-cognitive aggregates (mainly humans); this was one rationale for the SGDP. Additionally, KE is often an iterative process with many challenges. Thus, since KE can be seen as somewhat more art than engineering, there are no neat boundary lines as to what constitutes KE, with resultant controversies.

Of some import are these facts: KE has become closely allied with the field of artificial intelligence (AI); there is a division within the arena of KE between the transfer view of KE and the modeling view. It is beyond the scope of this article to explicate this division. For a more complete overview and discussion on KE, differing views and uses the reader is referred to Studer, Benjamins and Fensel (1998). Finally, there is a somewhat new emphasis on the KE/philosophical field of ontology as to building a model of a knowledge domain, defining the terms inside that domain and the relationships among them.

The Delphi Method (Process; Technique)

The Delphi method (sometimes referred to as a process or technique...all terms are somewhat accurate) is a structured KE technique, originally developed as a systematic, interactive forecasting method which relies on a panel of anonymous (to each other) experts. It has since been changed and expanded to become a tool for KE using experts in a variety of venues. The experts answer questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts’ forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of their panel. It is believed that during this process the range of the answers will decrease and the group will converge towards the “correct” answer. Finally, the process is stopped after a pre-defined stop criterion (e.g. number of rounds, achievement of consensus, stability of results) and the mean or median scores of the final rounds determine the results.

Key Terms in this Chapter

Ontology: (a) In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about concepts. (b) In philosophy, ontology is the study of the nature of being, becoming, existence , or reality , as well as the basic categories of being and their relations. Traditionally listed as a part of the major branch of philosophy known as metaphysics, ontology deals with questions concerning what entities exist or can be said to exist, and how such entities can be grouped, related within a hierarchy, and subdivided according to similarities and differences.

Artificial Intelligence: The branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes games playing : programming computers to play games such as chess and checkers; expert system s: programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms ); natural language : programming computers to understand natural human languages; neural networks: Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains and robotics : programming computers to see and hear and react to other sensory stimuli.

Domain Expert: A person with special knowledge or skills in a particular area of endeavor. The term domain expert is frequently used in expert systems software development.

Facilitator: A person who 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.

Groupthink: A term coined by social psychologist Irving Janis (1972). Occurs when a group makes faulty decisions because of group pressures; Groupthink is a psychological phenomenon that occurs within a group of people, in which the desire for harmony or conformity in the group results in an incorrect or deviant decision-making outcome.

Bandwagon Effect: A psychological phenomenon whereby people do something primarily because other people are doing it, regardless of their own beliefs, which they may ignore or override. This tendency of people to align their beliefs and behaviors with those of a group is also called “herd mentality.”

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

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 Throughout his career, Lewin was focused on how the study of group dynamics could be applied to real-world issues and problems.

Consensus: For the purpose of this article and as used in this article, 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 that they can support the consensus arrived at, no reservations.

Halo Effect: The halo effect was given its name by psychologist Edward Thorndike. It is a cognitive bias in which one's judgments of a person’s character can be influenced by one's overall impression of him or her. It can be found in a range of situations from the courtroom to the classroom and in everyday interactions.

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