Group topic modeling for academic knowledge discovery

Conference mining and expert finding are useful academic knowledge discovery problems from an academic recommendation point of view. Group level (GL) topic modeling can provide us with richer text semantics and relationships, which results in denser topics. And denser topics are more useful for academic discovery issues in contrast to Element level (EL) or Document level (DL) topic modeling, which produces sparser topics. Previous methods performed academic knowledge discovery by using network connectivity (only links not text of documents), keywords-based matching (no semantics) or by using semantics-based intrinsic structure of the words presented between documents (semantics at DL), while ignoring semantics-based intrinsic structure of the words and relationships between conferences (semantics at GL). In this paper, we consider semantics-based intrinsic structure of words and relationships presented in conferences (richer text semantics and relationships) by modeling from GL. We propose group topic modeling methods based on Latent Dirichlet Allocation (LDA). Detailed empirical evaluation shows that our proposed GL methods significantly outperformed DL methods for conference mining and expert finding problems.