Examples of Knowledge Application Systems

Knowledge Application Systems support the process through which some individuals utilize knowledge possessed by other individuals without actually acquiring, or learning, that knowledge.

Knowledge application technologies, which support direction and routines includes:

• expert systems
• decision support systems
• adviser systems
• fault diagnosis (or troubleshooting) systems
• help desk systems.

An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance of the expert however common to most or all are 1) the creation of a knowledge base which uses some knowledge representation formalism to capture the subject matter expert's knowledge and 2) a process of gathering that knowledge from the subject matter expert's and codifying it according to the formalism, which is called knowledge engineering. Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human subject matter expert, typically as an aid to human workers or a supplement to some information system.

A Decision Support System (DSS) is a class of information systems (including but not limited to computerized systems) that support business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions.Typical information that a decision support application might gather and present are:

• inventories of all of your current information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
• comparative sales figures between one week and the next,
• projected revenue figures based on new product sales assumptions.

Typical case-based knowledge application system will consist of the following processes: 

• Search the case library for similar cases.
• Select and retrieve the most similar case(s).
• Adapt the solution for the most similar case.
• Apply the generated solution and obtain feedback.
• Add the newly solved problem to the case library.