Related Knowledge Base Research Articles

Using anthropological interview strategies to enhance knowledge acquisition

Knowledge engineering is the “bottleneck” in expert systems (ES) development that has prevented widespread ES application. The bottleneck problem is exacerbated by a lack of specific information in published accounting research and accounting information texts describing specific knowledge elicitation activities. That lack of information adversely impacts the ability to develop cost effective ES. A review from the social sciences indicates that knowledge elicitation has been well developed by anthropologists and ethnologists. Application of anthropological interview strategies can overcome this “bottleneck.” Ford and Wood (1992) developed a strategy fof knowledge elicitation based on their research into anthropology and ethnology. Ford and Wood's (1992) four phase strategy addresses the issues of knowledge organization, problem representation, problem solving strategies, and tacit knowledge to improve the elicitation process. This four phase interviewing strategy was applied to an accounting and finance environment and a prototype ES was developed. Examples of the questions used to perform each knowledge elicitation task are presented along with a description of the controls used to assure accuracy in developing the related knowledge base. Using this elicitation strategy, allows one to overcome many of the problems previously identified with knowledge elicitation.

Explaining Financial Results

AbstractCROSBY III automatically constructs explanations for financial results. The key elements of the program are (1) a body of raw financial data to be explained, (2) an extensible knowledge base of financial relations expressed as algebraic constraints, (3) a selection of possible explanatory variables and information about the relative likelihoods of individual hypotheses, (4) an algorithm for generating consistent, adequate, plausible and parsimonious interpretations of the data, (5) a facility for explaining how a particular interpretation supports the data and (6) a facility for explaining why one interpretation is preferred over another. This paper focuses on facilites (5) and (6). CROSBY III is an implemented but undeployed prototype that has been tested on historical data and financial models of a small high‐technology company along with its closest competitors, on more than ten divisions of a large company and on a collection of large banks.

A Procedure for Automated Gas Turbine Blade Fault Identification Based on Spectral Pattern Analysis

A method for diagnosing the existence and the kinds of faults in blades of a gas turbine compressor is presented in the present paper. The innovative feature of this method is that it performs the diagnosis automatically, that is, it gives a direct answer to whether a fault exists and what type of fault it is, without requiring the interpretation of results by a human expert. This is achieved by derivation of the values of discriminants calculated from spectral patterns of fast response measurement data. A decision about the corresponding engine status is then derived according to the values of the discriminants. In the paper, the procedure of examining the suitability of particular parameter discriminants and the constitution of a related knowledge base is described. The derivation of decisions by a computer, and on what engine condition a particular measurement data set corresponds, are then described.

Design and evaluation of a relational knowledge base prototype machine

In this paper we describe a relational knowledge base prototype machine, which is based on the relational knowledge model. A unification mechanism is used as the retrieval mechanism. This machine consists mainly of a control processor (Intel 86/310), four processing units (Intel 86/12) dedicated to unification operations, a shared memory and a disk system. The performance evaluation of the system is also described. It is proven that for a given group of benchmarks the speed of the prototype is over 20 times higher than a PROLOG system running on an Intel 86/310.

Knowledge-based distribution system analysis and reconfiguration

The development of a knowledge-based software package for the analysis and control of electric distribution networks is discussed. A relational knowledge base, implemented in Prolog, is used to insure fast access to the data and an efficient post-reconfiguration update algorithm. An expert system shell DECIDE, is used to interpret human knowledge coded as knowledge clusters, and to provide a menu-driven user interface. The knowledge-based system described here has the following advantages: its expert system shell provides 'backup' and 'explanation' facilities; it can generate all the possible network reconfigurations and recommend the best choice; and the Prolog implementation of the algorithms is easy to maintain. The knowledge base is easy to expand and can be modified during execution. The prototype system demonstrates that artificial intelligence techniques can be used in power system analysis and reconfiguration, and provides a platform on which a base future research on the utility of expert system technology in distribution automation. >

The Mechanics of Representing Knowledge

This paper describes mechanical knowledge representation schemes found in several popular expert system building tools (ESBTs). In order to realize the full potential of ESBTs, it must be possible to develop a knowledge base in one ESBT and transfer the knowledge into another. Porting a knowledge base across ESBTs requires a clear understanding of the mechanical knowledge representation properties supported by each tool. In the following discussion, properties considered include: canonicity; truth value; plausibility, certainty, and possibility (PCP); temporality; and procedural knowledge. The nature of each property is described along with comments on related knowledge base characteristics. A summary table appears below relating these properties to several popular ESBTs. Overlap found in many of the mechanical knowledge representation properties suggests that automatic knowledge base translation is feasible.

A model and an architecture for a relational knowledge base

A relational knowledge base model and an architecture which manipulates the model are presented. An item stored in the relational knowledge base is called a term. A unification operation on terms in the relational knowledge base is used as the retrieval mechanism. The relational knowledge base architecture we propose consists of a number of unification engines, several disk systems, a control processor, and a multiport page-memory. The system has a knowledge compiler to support a variety of knowledge representations.

A method on software development and maintenance support with reference to documentation (1st report, Basic concept of the suport tool and contradiction detection methods in logic)

A basic concept of a software support tool for the production, maintenance and updating is presented employing a structure of the expert system interacting with both software and documentation. This support tool can include knowledge bases, data bases of target softwares, and also other related knowledge bases. As a first step toward this direction, a small sized support tool for software development has been developed by introducing software grammers, syntactics and semantics in a small world as rules and documents analizer. Three types of the contradiction detection method are incorporated in this system so as to detect contradictions in documentations and an additionally inserted knowledge in the expert system itself, based on the semantics and the knowleges, which are applicable for many expert systems. This system has two types of checking methods, such as a software based checking and a documentation based checking, depending upon the applications. Some experiments of the system have been carried out as a man-machine interface.