Using Linked Data Traversal to Label Academic Communities

In this paper we exploit knowledge from Linked Data to ease the process of analysing scholarly data. In the last years, many techniques have been presented with the aim of analysing such data and revealing new, unrevealed knowledge, generally presented in the form of ``patterns". However, the discovered patterns often still require human interpretation to be further exploited, which might be a time and energy consuming process. Our idea is that the knowledge shared within Linked Data can actuality help and ease the process of interpreting these patterns. In practice, we show how research communities obtained through standard network analytics techniques can be made more understandable through exploiting the knowledge contained in Linked Data. To this end, we apply our system Dedalo that, by performing a simple Linked Data traversal, is able to automatically label clusters of words, corresponding to topics of the different communities.