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Conference Paper

Relevance Feedback in XML Retrieval

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons45158

Pan,  Hanglin
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Citation

Pan, H. (2004). Relevance Feedback in XML Retrieval. In Current trends in database technology, EDBT 2004 Workshops: EDBT 2004 Workshops PhD, DataX, PIM, P2P&DB, and ClustWeb (pp. 187-196). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-2B21-C
Abstract
Highly heterogeneous XML data collections that do not have a global schema, as arising, for example, in federations of digital libraries or scientific data repositories, cannot be effectively queried with XQuery or XPath alone, but rather require a ranked retrieval approach. As known from ample work in the IR field, relevance feedback provided by the user that drives automatic query refinement or expansion can often lead to improved search result quality (e.g., precision or recall). In this paper we present a framework for feedback-driven XML query refinement and address several building blocks including reweighting of query conditions and ontology-based query expansion.We point out the issues that arise specifically in the XML context and cannot be simply addressed by straightforward use of traditional IR techniques, and we present our approaches towards tackling them.