Knowledge extraction expert systems

Knowledge Extraction. Expert systems are usually used to solve hard" problems for which the solution methodology is not documented. An expert is a person who can provide the highest quality answers or advice for a specific problem domain. Unless the expert routinely teaches the problem-solving method, he or she will probably have difficulty in clearly describing the method. 

While ANNs cannot do anything that would be impossible to accomplish using an alternative algorithmic method, they can execute tasks that would otherwise be very difficult, such as forming a model from sensory data or from data that is extracted from a continuous industrial process for which no comprehensive theoretical model exists. Their principal limitation is that the numerical model that they create, while open to inspection, is difficult to interpret. This is in marked contrast to the model used by expert systems, in which the knowledge of the system is expressed in readily interpreted statements of fact and logical expressions. Nevertheless, the power of the ANN is... 

The information to which the rule is applied might be extracted from the knowledge base, it might be provided by the user in response to questions from the ES, or it may be provided by combining the two. An expert system that uses rule-based reasoning is, quite reasonably, known as a rule-based system. This is the most widely used form of expert system in science, and it is on this type of system that this chapter concentrates. 

RuleMaker is a knowledge extraction utility for building and testing the decision logic contained within Radial modules. The logic is specified as a table of examples of correct expert decisions for each module. RuleMaker transforms each example set into an equivalent decision tree, and automatically generates the body of the module in the form of Radial code. System builders may also choose to enter Radial code directly, although they usually prefer to work with example tables. 

Expert systems offer the possibility of revolutionizing the practice of chemistry. There are, however, two major problems which must be overcome. The first, which is easy to overlook until you develop your first expert system, is the time required to extract the knowledge from the expert and encode it into an expert system. The second major problem is the amount of computation required to solve a given problem. This problem is compounded because the most useful applications tend to be the most complex. 

The ability to automatically acquire, check, and process knowledge is of the utmost importance. KEY provides the expert with a way by which individual pieces of knowledge can be extracted and then fused together into a working expert system without requiring the services of a knowledge engineer. 

One of the great difficulties in the development and construction of an expert system is attributed to the extraction of expert knowledge. An initial problem is the communication between the expert and the knowledge engineer. The engineer can perform a series of interviews without have good grasp of the subject matter. 

A database containing the codified knowledge of an expert. It is used as a basis for the intelligent behavior of artificial intelligence systems. The knowledge is compiled by experts or is extracted in a process of knowledge acquisition. It is a central part of expert systems. See Structure Determination by Computer-based Spectrum Interpretation and Synthesis Design. 

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