Know-What and Know-Who: Document Searching and Exploration using Topic-Based Two-Mode Networks

This paper proposes a novel approach for analyzing search results of a document collection, which can help support know-what and know-who information seeking questions. Search results are grouped by topics, and each topic is represented by a two-mode network composed of related documents and authors (i.e., biclusters). We visualize these biclusters in a 2D layout to support interactive visual exploration of the analyzed search results, which highlights a novel way of organizing entities of biclusters. We evaluated our approach using a large academic publication corpus, by testing the distribution of the relevant documents and of lead and prolific authors. The results indicate the effectiveness of our approach compared to traditional 1D ranked lists. Moreover, a user study with 12 participants was conducted to compare our proposed visualization, a simplified variation without topics, and a text-based interface. We report on participants’ task performance, their preference of the three interfaces, and the different strategies used in information seeking.