Document Type



This item is available under a Creative Commons License for non-commercial use only


Computer Sciences

Publication Details

Sucessfully submitted for the award of M.Sc. in Computing (Knowledge Managment) to the Technological University Dublin, August 2009.


Management itself is not always clearly understood, however, the confusion can be sorted if the underlying theory is clearly distinguished from its implementations. There also may be a specific role for computing in Knowledge Management. It is suggested that Knowledge Management technologists seek a unique strategic position for Computing in Knowledge Management, for reasons of clarity and differentiation from Information Systems. The effective transfer of knowledge is one of the main themes or core competency in Knowledge Management. This is also one of the areas where Knowledge Management technologists may find the niche for a distinct and unique contribution the field of computing can provide. This dissertation finds that Computing for Knowledge Management can be seen as the meeting of the computer sciences, cognitive psychology and sociology since this combination is fit to produce a uniquely user centric outcome. Scientists working the field of Visualisation have already created the foundations on which Knowledge Management technologists may start building. This applies particularly to its subsection of Knowledge Visualisation and by extension to Knowledge Maps. This dissertation investigates the advantages of Knowledge Management tools based on Knowledge Maps, how such a system might be implemented and what issues in terms of people, process and technology must be expected to arise. An experiment was conducted to find out about these issues. The same knowledge base content was presented to two groups of users, one group was presented with a tabular yellow pages type of knowledge map; the other was presented with a graphic knowledge map interface. Participants had to perform a search task and were given a recall test. The experiment tool recorded user activity and answers given for statistical analysis. Participation was too low to lead to any conclusions but the experiment still yielded useful results.