End‐user access to multiple sources: incorporating knowledge discovery into knowledge management

Abstract

The End-User Access to Multiple Sources-EAMS system-integrates given information sources into a knowledge management system. It relates the world of documents with the database world using an ontology. The focus of developing the EAMS system is on the acquisition and maintenance of knowledge. Hence, in both worlds, machine learning is applied. In the document world, a learning search engine adapts to user behaviour by analysing the click-through-data. This eases the personalization of selecting appropriate documents for users and does not require further maintenance. In the database world, knowledge discovery in databases (KDD) bridges the gap between the fine granularity of relational databases and the actual information needs of users. KDD extracts knowledge from data and, therefore, allows the knowledge management system to make good use of already existing company data-without further acquisition or maintenance. A graphical user interface provides users with a uniform access to document collections on the Internet (Intranet) as well as to relational databases. Since the ontology generates the items in the user interface, a change in the ontology automatically changes the user interface without further efforts.The EAMS system has been applied to customer relationship management in the insurance domain. Questions to be answered by the system concern customer acquisition (e.g. direct marketing), customer up-and cross-selling (e.g. which products sell well together), and customer retention (here, which customers are likely to leave the insurance company or ask for a return of a capital life insurance). Documents about other insurance companies and demographic data published on the Internet contribute to the answers, as do the results of data analysis of the company's contracts.Knowledge management supplies information organization-wide to very different users. If

A Review of Literature on Human Behaviour and Artificial Intelligence: Contributions Towards Knowledge Management

“…Within this timespan articles focused on machine learning and artificial intelligence for fuzzy models for scheduling problems and decisions processes in manufacturing diminishing human error (Nematii, 2002;Akyol, 2004); predicting gaming behaviour (Hinnar et al, 2004); behavioural simulation for coaching purposes (Summers, 2004); the role of mind in human and organizational behaviour (Seni and Seni, 2000;Setterret, 2002;Wolozin, 2004); planned (Bamberg, 2003) and consumer (Morik, 2002) behaviour.…”