Knowledge-Based Systems (KBS) development standards: A maintenance perspective

Information on knowledge-based systems (KBS) is given in viewgraph form. Information is given on KBS standardization needs, the knowledge engineering process, program management, software and hardware issues, and chronic problem areas

Research into the development of a knowledge acquisition taxonomy

The focus of the research was on the development of a problem solving taxonomy that can support and direct the knowledge engineering process during the development of an intelligent tutoring system. The results of the research are necessarily general. Being only a small initial attempt at a fundamental problem in artificial intelligence and cognitive psychology, the process has had to be bootstrapped and the results can only provide pointers to further, more formal research designs

Circuit Minimization Techniques Applied to Knowledge Engineering

Often knowledge engineers encounter situations during the interviewing process in which experts have difficulty expressing the knowledge to be captured. In these situations, the experts cannot readily present their knowledge so that the knowledge engineers can encode it in the chosen formalism (for example, in production rules). During the development of an expert system for underwriting homeowner insurance policies, this situation was occasionally encountered. When the experts could not express their knowledge in chunks suitable for encoding directly in production rules, circuit minimization techniques were used to construct the set of production rules from exhaustive tables of acquired knowledge. The techniques also served to find errors in the acquired knowledge. Circuit minimization techniques, therefore, have been found to provide valuable assistance in the knowledge engineering process, both in the acquisition and verification of knowledge

Unified Enterprise Knowledge Representation with Conceptual Models - Capturing Corporate Language in Naming Conventions

Conceptual modeling is an established instrument in the knowledge engineering process. However, a precondition for the usability of conceptual models is not only their syntactic correctness but also their semantic comparability. Assuring comparability is quite challenging especially when models are developed by different persons. Empirical studies show that such models can vary heavily, especially in model element naming, even if they are meant to express the same issue. In contrast to most ontology-driven approaches proposing the resolution of these differences ex-post, we introduce an approach that avoids naming differences in conceptual models already during modeling. Therefore we formalize naming conventions combining domain thesauri and phrase structures based on a linguistic grammar. This allows for guiding modelers automatically during the modeling process using standardized labels for model elements, thus assuring unified enterprise knowledge representation. Our approach is generic, making it applicable for any modeling language

Anomalous event diagnosis for environmental satellite systems

The National Oceanic and Atmospheric Administration's (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) is responsible for the operation of the NOAA geostationary and polar orbiting satellites. NESDIS provides a wide array of operational meteorological and oceanographic products and services and operates various computer and communication systems on a 24-hour, seven days per week schedule. The Anomaly Reporting System contains a database of anomalous events regarding the operations of the Geostationary Operational Environmental Satellite (GOES), communication, or computer systems that have degraded or caused the loss of GOES imagery. Data is currently entered manually via an automated query user interface. There are 21 possible symptoms (e.g., No Data), and 73 possible causes (e.g., Sectorizer - World Weather Building) of an anomalous event. The determination of an event's cause(s) is made by the on-duty computer operator, who enters the event in a paper based daily log, and by the analyst entering the data into the reporting system. The determination of the event's cause(s) impacts both the operational status of these systems, and the performance evaluation of the on-site computer and communication operations contractor

Using XML and XSLT for flexible elicitation of mental-health risk knowledge

Current tools for assessing risks associated with mental-health problems require assessors to make high-level judgements based on clinical experience. This paper describes how new technologies can enhance qualitative research methods to identify lower-level cues underlying these judgements, which can be collected by people without a specialist mental-health background. Methods and evolving results: Content analysis of interviews with 46 multidisciplinary mental-health experts exposed the cues and their interrelationships, which were represented by a mind map using software that stores maps as XML. All 46 mind maps were integrated into a single XML knowledge structure and analysed by a Lisp program to generate quantitative information about the numbers of experts associated with each part of it. The knowledge was refined by the experts, using software developed in Flash to record their collective views within the XML itself. These views specified how the XML should be transformed by XSLT, a technology for rendering XML, which resulted in a validated hierarchical knowledge structure associating patient cues with risks. Conclusions: Changing knowledge elicitation requirements were accommodated by flexible transformations of XML data using XSLT, which also facilitated generation of multiple data-gathering tools suiting different assessment circumstances and levels of mental-health knowledge