Quality Scores for Queries: Structured Data, Synthetic Queries and Augmentation Queries

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Quality Scores and Augmentation Queries

In general, the subject matter of this specification relates to identifying or generating augmentation queries, storing the augmentation queries, and identifying stored augmentation queries for use in augmenting user searches. An augmentation query can be a query that performs well in locating desirable documents identified in the search results. The performance of an augmentation query can be determined by user interactions. For example, if many users that enter the same query often select one or more of the search results relevant to the query, that query may be designated an augmentation query.

In addition to actual queries submitted by users, augmentation queries can also include synthetic queries that are machine generated. For example, an augmentation query can be identified by mining a corpus of documents and identifying search terms for which popular documents are relevant. These popular documents can, for example, include documents that are often selected when presented as search results. Yet another way of identifying an augmentation query is mining structured data, e.g., business telephone listings, and identifying queries that include terms of the structured data, e.g., business names.

These augmentation queries can be stored in an augmentation query data store. When a user submits a search query to a search engine, the terms of the submitted query can be evaluated and matched to terms of the stored augmentation queries to select one or more similar augmentation queries. The selected augmentation queries, in turn, can be used by the search engine to augment the search operation, thereby obtaining better search results. For example, search results obtained by a similar augmentation query can be presented to the user along with the search results obtained by the user query.

This past March, Google was granted a patent that involves giving quality scores to queries (the quote above is from that…

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