Relevance Feedback for Structural Query Expansion

Keyword-based queries are an important means to retrieve information from XML collections with unknown or complex schemas. Relevance Feedback integrates relevance information provided by a user to enhance retrieval quality. For keyword-based XML queries, feedback engines usually generate an expanded keyword 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 pure content-based to structural feedback. It presents two independent approaches that include structural dimensions in a feedback-driven query evaluation: The first approach reranks the result list of a keyword-based search engine, using structural features derived from results with known relevance. The second approach expands a keyword query into a full-fledged content-and-structure query with weighted conditions.

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