Approximate query mapping: Accounting for translation closeness

In this paper we present a mechanism for approximately translating Boolean query constraints across heterogeneous information sources. Achieving the b est translation is challenging because sources support different constraint s for formulating queries, and often these constraints cannot be precisely translated. For instance, a query [score >8] might be "perfectly" translate d as [rating > 0.8] at some site, but can only be approximated as [grade = A] at another. Unlike other work, our general framework adopts a customizab le "closeness" metric for the translation that combines both precision and recall. Our results show that for query translation we need to handle inter dependencies among both query conjuncts as well as disjuncts. As the basis, we identify the essential requirements of a rule system for users to encod e the mappings for atomic semantic units. Our algorithm then translates com plex queries by rewriting them in terms of the semantic units. We show that , under practical assumptions, our algorithm generates the best approximate translations with respect to the closeness metric of choice. We also prese nt a case study to show how our technique may be applied in practice.