Today, it is recognized that knowledge is one of the most important assets of organizations. It is a challenge to be able to manage these knowledge assets. Advanced knowledge management requires thorough analyses and interpretation of all available data either of a technical or a non-technical nature pertaining to one or more application domains and of any type such as a linguistic data type, an image data type, a video data type, a sound data type, a control data type, a measurement data type, olfactive and tactile data types. Knowledge regarding processes, products, markets, technologies and the organization likewise have to be processed. This ultimately enables the organizations to make profit.
Most information technology (IT) employed to enable knowledge work appears to target data and information, as opposed to knowledge itself. Present IT systems used to support knowledge management are limited primarily to conventional database management systems (DBMS), data warehouses and data mining tools (DW/DM), intranet/extranet and groupware.
In these existing systems the underlying representation of the reality domain that is used as a starting and reference point for the supported knowledge related activity is based on predefined conceptual representations. This implies that all of these applications are more or less strictly related and restricted to specific knowledge domains and that dealing with the every day increasing list of new concepts within all knowledge domains is both a time consuming manual job and a computationally complex process.
It needs to be observed that a number of the known systems even miss the power to account of the relational nature of knowledge. Further knowledge doesn't consist of bare lists of concepts but knowledge is generally considered to be a network of explicitly related concepts.