Cluster-lift method for mapping research activities over a concept tree
The paper builds on the idea by R. Michalski of inferential concept interpretation<br>for knowledge transmutation within a knowledge structure taken here to<br>be a concept tree. We present a method for representing research activities within a<br>research organization by doubly generalizing them. To be specific, we concentrate<br>on the Computer Sciences area represented by the ACM Computing Classification<br>System (ACM-CCS). Our cluster-lift method involves two generalization steps: one<br>on the level of individual activities (clustering) and the other on the concept structure<br>level (lifting). Clusters are extracted from the data on similarity between ACMCCS<br>topics according to the working in the organization. Lifting leads to conceptual<br>generalization of the clusters in terms of “head subjects” on the upper levels of<br>ACM-CCS accompanied by their gaps and offshoots. A real-world example of the<br>representation is provided.