Specification of expertise

In critical infrastructure applications, timely and consistent fault detection and diagnosis is an increasingly important operational process, especially in the energy sector where safety is of the utmost importance. To realise this, engineers have to manually analyse data acquired from several assets using predefined diagnostic processes, but this is a time-consuming process requiring significant amounts of specialist expert knowledge. Data-driven approaches to support fault detection and diagnosis, and other similar problems, can produce accurate results comparable to what the engineers can achieve in a fraction of the time. However, the majority of these data-driven techniques are black box techniques and lack explainability which is often necessary for explaining decisions about critical assets in the power generation industry. Knowledge-based systems, such as rule-based expert systems have been shown to provide not only accurate decisions but also the explanation and reasoning behind these decisions in some related applications. However, there is a significant time cost associated with the development of knowledge-based systems, and in particular with the knowledge elicitation process, where the domain expert’s knowledge is formalised and is encoded into the system. This challenge is commonly referred to as the knowledge elicitation bottleneck.

In this paper, we present a novel approach to performing the knowledge elicitation using a set of symbolic primitives (rise, fall, fluctuate, and stable) to parameterise typical time-series condition monitoring data. The knowledge is represented by using a common language that can easily be communicated with (and from) the domain experts. This allows for the quick and accurate elicitation of the domain experts knowledge, but also the formalisation and implementation of the knowledge into a rapidly produced diagnostic system. Further to this, due to the parametrisation of the knowledge, it is possible to iteratively improve the knowledge base by updating these parameters based on new unseen data. This approach was applied to the Tennessee Eastman dataset, which is simulated data of a real-world industrial process. It was found that by using this approach it was possible to accurately and quickly capture the knowledge required to detect several faults within the case study dataset, but also provided fully explained reasons why each fault was detected by relating the explanations to the symbolic primitives previously defined.

Public organizations produce daily a great volume of administrative documents, in order to fulfill their mission. This requires the use of a certain, unique for each procedure, legal framework. This article adopts a process oriented approach, through a web-based knowledge management system that provides this legal framework in an up-to-date and accurate manner. The system also supports the interpretation of the legal framework, supplying civil servants, citizens and businesses with precedents and opinions. The system employs an ontology in OWL for representing the public administration structure and any kind of document that flows among administrative units, during the execution of the procedures, which are mapped into OWL-S service models.

Knowledge is either explicit or tacit. The elicitation, codification, storage, and distribution of tacit knowledge are extremely challenging tasks that require innovative methods and techniques. This paper reports the results of a study in which the tacit knowledge of domain experts was elicited, represented, and analyzed for validity. The subjects were a group of instructors and students at a USPS training school whose memory structures were analyzed for evidence of two common characteristics of expertise: holistic perception and use of abstract concepts. No evidence of either characteristic was found in the more experienced instructor group but, when the subjects were regrouped based on observed performance, the cognitive models of the high performers contained structural evidence of both characteristics. This finding led to the conclusion that experience alone is not an indicator of expertise. Other factors, such as the cognitive ability to correctly structure those experiences, must also be present.

Changes in an organization's internal and external environment impose numerous demands on management information systems (MIS). Since few MIS can be designed to fully anticipate all change, system portfolios frequently tend to become heavily weighted with older systems needing maintenance and revision. To maintain cost effectiveness, encourage system reuse, and ensure that the new system's behavior will differ from that of the old one only as intended, the former's functional design must be recovered (preferably from the old program code itself) when undertaking such maintenance and revision. We studied of how experts perform this design recovery activity by analyzing the cognitive processes of six experienced system developers engaged in the program comprehension phase of software reengineering. Results of protocol analysis indicate that subjects distributed their cognitive processes in a systematic and generally comparable fashion. Over the sample, similar transitions between two activities and patterns of three activities were observed to occur with high frequency. The most frequently occurring program comprehension transitions and patterns generally occurred within, rather than across, two activity groups: recognition and design documentation. We conclude with suggestions for further research on program comprehension and the design of computerized tools to support the software reengineering process.

Database design is a complex and time-consuming process. In order to automate database design, an understanding of the nature of expertise that goes into the design process is needed. Although a number of expert systems have been developed to assist or replace a database designer, database design expertise has not been examined in any detail. This paper proposes a conceptual framework for explaining this type of expertise. The components of the framework are applied to each phase of the design process and used to provide guidelines for the level of expertise developers might strive to obtain. Several representative systems are analyzed, based on the framework, to explore the degree to which expertise is being captured. Implications for the future development of database design expert systems are discussed.

The psychological study of expertise has a rich background and has recently gained impetus in part because of the advent of expert systems and related technologies for preserving knowledge. In the study of expertise, whether in the context of applications or the context of psychological research, knowledge elicitation is a crucial step. Research in a number of traditions - judgment and decision making, human factors, cognitive science, expert systems - has utilized a variety of knowledge elicitation methods. Given the diversity of disciplines, topics, paradigms, and goals, it is difficult to make the literature cohere around a methodological theme. For discussion purposes, we place knowledge elicitation techniques into three categories: (1) analysis of the tasks that experts usually perform, (2) various types of interviews, and (3) contrived tasks which reveal an expert′s reasoning processes without necessarily asking about these processes. We illustrate types and subtypes of techniques, culminating in a discussion of research that has empirically evaluated and compared techniques. The article includes some recommendations about "how to do" knowledge elicitation, some cautionary tales, and a discussion of the prospects.