Automatically Mining Facets for Queries from their Search Results

Abstract

Automatically Mining Facets for Queries from Their Search Results Project report in Web mining Web search queries are often ambiguous or multi-faceted, which makes a simple ranked list of results inadequate. To assist information finding for such faceted queries, we explore a technique that explicitly represents interesting facets of a query using groups of semantically related terms extracted from search results.

As Automatically Mining Facets for Queries from Their Search Results project report an example, for the query “baggage allowance”, these groups might be different airlines, different flight types (domestic, international), or different travel classes (first, business, economy). We name these groups query facets and the terms in these groups facet terms. We develop a supervised approach based on a graphical model to recognize query facets from the noisy candidates found.

Conclusion

Automatically Mining Facets for Queries from Their Search Results Project report in web mining We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by aggregating frequent lists from free text, HTML tags, and repeat regions within top search results. We developed a supervised method based on a graphical model to recognize query facets from the noisy facet candidate lists extracted from the top ranked search results. We proposed two algorithms for approximate inference on the graphical model.

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