Vision

Like object technology, knowledge technology originates from a different view on software. Most software nowadays consists of programmes executing prescribed procedures or algorithms. Programmes can therefore only support and execute well-structured processes. These procedures must be exactly prescribed in advance. The order in which the user performs his actions is also prescribed. The behaviour, in other words, of a well-structured COBOL programme. Object technology in combination with a graphical user interface does away with the necessity of formally prescribing the order and coherence of the actions. Each function an object performs, however, remains a form of formally prescribed behaviour.
Knowledge technology focuses on the development of applications that simulate or support matters such as human reasoning, patter recognition and the human learning process. These are all applications with weak structures. Neither the order of the actions nor the way of acting are known exactly in advance.

Reasoning Systems
An important area of application for knowledge technology is the development of applications that simulate or support human reasoning. This kind of applications was originally facilitated by declarative programming languages such as LISP or PROLOG. With these languages it is possible to construct systems with reasoning rules, for instance with regard to diagnosing illnesses. The user presents a problem to the system, for example the symptoms a patient shows. On the basis of the problem, the system applies the right reasoning rules and arrives at a solution, consisting in this case of the probable diagnosis.

Nowadays, so-called hybrid development environments are used in knowledge technology. These are environments in which the developer can develop in a procedural, a rule-based and an object-oriented manner all at the same time.

An example of an integrated application is a module for the acceptance of insurance policies, developed for a large number of insurance companies. The insurance agent, who wants to hand in a policy application with a certain company, works with his own, usual software. Behind the scenes, a knowledge-based technology module assesses the application and, if acceptance is in order, processes it immediately at the agent's workstation. The policy, the premium calculation and the invoice are made immediately after acceptance of the policy. Knowledge technology has reduced the process of acceptance from several weeks to several minutes.

Neural Networks
Within the field of knowledge technology, an increasing amount of different technologies are used besides reasoning programmes, for example neural networks and genetic algorithms. These are forms of so-called fuzzy logic.
Neural networks imitate the working of the human brain. The brain consists of a network of interconnected brain cells (neurons) that are together capable of learning, remembering and especially recognising things. Artificial neural networks are mainly applied for solving problems to do with pattern recognition, such as the recognition of handwriting or voice recognition.

The knowledge in a neural network is not programmed but trained. A neural network only recognised a signature after 'seeing' it for a certain number of times and after learning from the user which person belongs with it. This training can, by the way, be done by the computer. To compensate the disadvantage of the time this training takes, certainly at first, there is the advantage that it is easy to add new knowledge to the network (a new signature, for example) by means of follow-up training.

An example of a neural network in combination with a knowledge technology system is a system supporting unemployed people in finding a job. On the basis of the profile of the unemployed person and the market situation, the neural network estimates the expected duration of the unemployment.

Genetic Algorithms
Genetic algorithms are self-learning algorithms. The idea is, for example, to have algorithms simulate a certain real-world situation as well as possible. In doing so, they are not only capable of representing the past course of events, but also of calculating the expected future course of events. One starts out with a certain set of algorithms, and after a more or less artificial selection of algorithms the set is found which best represents the behaviour of the problem situation. The finding of the right algorithms strongly resembles a biological evolution process with phenomena such as selection and cross-over and resulting in the 'survival of the fittest' of algorithms.

An example of an application that uses genetic algorithms is an application in which genetic algorithms are applied to the results of a credit score system in order to determine which algorithm best predicts a client's chance of failure.

Knowledge Management
To a certain extent, developing knowledge-based systems like reasoning systems, neural networks and genetic algorithms is already a form of knowledge management. After all, knowledge-based systems offer the possibility to record, distribute and maintain certain knowledge and experience of individuals within an organisation. The grip an organisation thus gets on knowledge grows, and people can (strategically) determine where, when and how the knowledge shall be used.

When referring to knowledge management systems, however, one rather means systems that support the collective knowledge and experience of an organisation. Employees from various departments and disciplines can access a central knowledge base, which supports the execution of various tasks. This knowledge base is a database containing data and objects that support the users in exchanging knowledge. Important with this type of systems are the management and the maintenance of the data relating to the knowledge of the users. Large organisations often appoint a knowledge manager to handle these tasks.

A knowledge management system was developed for one of the Dutch government departments. The application is used as a central working memory for lawyers. Through the system they have access to data that may support them in the execution of new tasks. The application uses knowledge technology in combination with retrieval techniques such as hypertext and full text retrieval. Knowledge technology was applied here for the classification, interpretation and conceptualisation of data within the knowledge domain. Besides this, 'knowledge technological' interview techniques were used to extract the knowledge.

Conclusion
In the coming years, knowledge technology will become an essential part of information technology, primarily as a technology to model and automate less structured tasks such as assessment and diagnosing, and secondly to manage stored data that support people's knowledge and to keep these data accessible. Such knowledge management systems are indispensable to keep the enormous amounts of distributed digital data accessible in the future. In the long run, the information superhighway will lead to the existence of a world-wide network of computers on which we will store and manage much of our knowledge in the form of digital data. Knowledge technology will be essential to be able to realise this in an effective, beneficial way.