IKEA: Unsupervised domain-specific keyword-expansion

Joobin Gharibshah, Jakapun Tachaiya, Arman Irani, Evangelos E. Papalexakis, Michalis Faloutsos

Abstract: How can we expand an initial set of keywords with a target domain in mind? A possible application is to use the expanded set of words to search for specific information within the domain of interest. Here, we focus on online forums and specifically security forums. We propose IKEA, an iterative embedding-based approach to expand a set of keywords with a domain in mind. The novelty of our approach is three-fold: (a) we use two similarity expansions in the word-word and post-post spaces, (b) we use an iterative approach in each of these expansions, and (c) we provide a flexible ranking of the identified words to meet the user needs. We evaluate our method with data from three security forums that span five years of activity and the widely-used Fire benchmark. IKEA outperforms previous solutions by identifying more relevant keywords: it exhibits more than 0.82 MAP and 0.85 NDCG in a wide range of initial keyword sets. We see our approach as an essential building block in developing methods for harnessing the wealth of information available in online forums.