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Feedback-Driven Structural Query Expansion for Ranked Retrieval of XML Data

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45609

Theobald,  Martin
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Schenkel, R., & Theobald, M. (2006). Feedback-Driven Structural Query Expansion for Ranked Retrieval of XML Data. In Advances in Database Technology - EDBT 2006: 10th International Conference on Extending Database Technology (pp. 331-348). Berlin, Germany: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-22D7-9
Zusammenfassung
Relevance Feedback is an important way to enhance retrieval quality by integrating relevance information provided by a user. In XML retrieval, feedback engines usually generate an expanded query from the content of elements marked as relevant or nonrelevant. This approach that is inspired by text-based IR completely ignores the semistructured nature of XML. This paper makes the important step from content-based to structural feedback. It presents an integrated solution for expanding keyword queries with new content, path, and document constraints. An extensible framework evaluates such query conditions with existing keyword-based XML search engines while allowing to easily integrate new dimensions of feedback. Extensive experiments with the established INEX benchmark show the feasibility of our approach.