Rough Set-Based Concept Mining from Social Networks

Motivated by successful applications of rough set theory to symbolic knowledge discovery from data tables, we would like to extend the approach to description logic (DL)-based mining of social networks. Unlike classical rough set theory, in which the attribute values of objects fully determine the indiscernibility relation, the rough set analysis of social networks must account for the social relationships between objects as well as their attributes. In this paper, the indiscernibility relation is defined by using the notions of positional equivalences in social network analysis. The indiscernibility relation can partition the universe of a social network into elementary sets which are used to define the lower and upper approximations of an arbitrary concept as in classical rough set theory. To induce concept definitions from such approximations, we use DL to represent knowledge discovered from social networks and present a constructive procedure to find a characterizing DL concept terms for each elementary set. Because the lower and upper approximations of a target concept are unions of elementary sets, we can use the disjunction of such characterizing concept terms to describe the definition of the target concept. This leads to a complete process of DL-based concept mining from social networks.