Metric Spaces for Temporal Information Retrieval

Documents and queries are rich in temporal features, both at the meta-level and at the content-level. We exploit this information to define temporal scope similarities between documents and queries in metric spaces. Our experiments show that the proposed metrics can be very effective for modeling the relevance for different search tasks, and provide insights into an inherent asymmetry in temporal query semantics. Moreover, we propose a simple ranking model that combines the temporal scope similarity with traditional keyword similarities. We experimentally show that it is not worse than traditional keyword-based rankings for non-temporal queries, and that it improves the overall effectiveness for time-based queries.