In an empirical user study, we assessed two approaches to ranking the results from a keyword search using semantic contextual match based on Latent Semantic Analysis. These techniques involved searches initiated from words found in a seed document within a corpus. The first approach used the sentence around the search query in the document as context while the second used the entire document. With a corpus of 20,000 documents and a small proportion of relevant documents (<0.1%), both techniques outperformed a conventional keyword search on a recall-based information retrieval (IR) task. These context-based techniques were associated with a reduction in the number of searches conducted, an increase in users’ precision and, to a lesser extent, an increase in recall. This improvement was strongest when the ranking was based on document, rather than sentence, context. Individuals were more effective on the IR task when the lists returned by the techniques were ranked better. User performance on the task also correlated with achievement on a generalized IQ test but not on a linguistic ability test.

Formal Concept Analysis (FCA) derives a multiple-inheritance class hierarchy from a formal context. The number of classes is bounded above by an exponential function of the number of objects and attributes in the context. To support interactive analysis of large formal contexts, this paper exploits a divide-and-conquer technique which discovers hierarchical structure in amenable formal contexts. That hierarchical structure is used to expedite and enhance both the layout of, and user interaction with, the concept lattice. The principal contribution is the dual use of the discovered hierarchical structure for scalable, interactive FCA.