Efficient Top-k Document Retrieval Using a Term-Document Binary Matrix

Current web search engines perform well for "navigational queries." However, due to their use of simple conjunctive Boolean filters, such engines perform poorly for "informational queries." Informational queries would be better handled by a web search engine using an informational retrieval model along with a combination of enhancement techniques such as query expansion and relevance feedback, and the realization of such a engine requires a method to prosess the model efficiently. In this paper, we describe a novel extension of an existing top-k query processing technique. We add a simple data structure called a "term-document binary matrix," resulting in more efficient evaluation of top-k queries even when the queries have been expanded. We show on the basis of experimental evaluation using the TREC GOV2 data set and expanded versions of the evaluation queries attached to this data set that the expanded technique achieves significant performance gains over existing techniques.