Effective Strategies for Temporally Anchored Information Retrieval
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A number of emerging large scale applications such as web archiving and time-stamped web objects generated through information feeds involve time-evolving objects that can be most effectively explored through search within a temporal context. We develop in this paper a new approach to handle the temporal text search of a time evolving collection of documents. Specifically, given a temporally anchored query, our method will return a ranked set of documents that were live during the query time span and the relevance scores are computed relative to the state of the collection as it existed during the query time span. Our approach introduces both a new indexing organization that substantially limits the search space and an effective methodology for computing the temporally anchored relevance scores. Moreover, we develop an analytical model that can be used to determine the temporal granularity of the indexing organization which minimizes the total number of postings examined during query evaluation. Our approach is validated through extensive empirical results generated using two very different and significant datasets.