From the website:
Traditional interactive information retrieval systems function by creating inverted lists, or term indexes. For every term in the vocabulary, a list is created that contains the documents in which that term occurs and its frequency within each document. Retrieval algorithms then use these term frequencies alongside other collection statistics to identify matching documents for a query.
Term-based search, however, is just one example of interactive information seeking. Other examples include offering suggestions of documents similar to ones already found, or identifying effective query expansion terms that the user might wish to use. More generally, these fall into several categories: query term suggestion, relevance feedback, and pseudo-relevance feedback.
We can combine the inverted index with the notion of retrievability to create an efficient query expansion algorithm that is useful for a number of applications, such as query expansion and relevance (and pseudo-relevance) feedback. We call this kind of index a reverted index because rather than mapping terms onto documents, it maps document ids onto queries that retrieved the associated documents.
As to its performance:
….the short answer is that our query expansion technique outperforms PL2 and Bose-Einstein algorithms (as implemented in Terrier) by 15-20% on several TREC collections. This is just a first stab at implementing and evaluating this indexing, but we are quite excited by the results.
An interesting example of innovative thinking about indexing.
With a useful result.