Google Inc.

Paul Haahr

Simon Tong

Jeffrey Adgate Dean

Steven D Baker

Karl Pfleger

Steve Lawrence

Monika Hildegard Henzinger

Amit Singhal

Olcan Sercinoglu

Yonghui Wu

Uri N Lerner

Michael E Flaster

Anurag Acharya

Matthew D Cutts

Krishna Asur Bharat

Amitabh Singhal

Matt Cutts

Monika Henzinger

Charles E Martin

Nitin Kumar Gupta

Bryan Christopher Horling

John Ogden Lamping

Srinivasan Venkatachary

Daniel Trawick Egnor

Philip Andrew McDonnell

Owen Daniel Otto

Kavi J Goel

Ke Yang

Michael Y Brukman

Radhika Malpani

Vibhu Mittal

Sundeep Tirumalareddy

Kevin David Lacker

Eric Lehman

Randall G Keller

Muthuprasanna Muthusrinivasan

Marcin Kaszkiel

Misha Zatsman

Jun Xu

Anna Lynn Patterson

Hyung-Jin Kim

Trevor D Strohman

Gabriel Schine

Urs Hoelzle

Sushrut Suresh Karanjkar

Chung Tin Kwok

Moustafa A Hammad

Jay Shrauner

Adam Sadovsky

Per Bjornsson

Kevin Lackner

Martin Kaszkiel

Kien Ng

Amitabh K Singhal

Daniel Egnor

Kevin Lacker

In general, one aspect of the subject matter described can be embodied in a method that includes, obtaining a plurality of search results responsive to an initial search query, the search results including a first search result that identifies a first resource; determining, using a document-to-query-to-document model, that the first resource is relevant to a first suggested query different from the initial search query; generating a presentation of the search results responsive to the initial search query; and providing the presentation of the search results in response to the initial search query. Each search result in the presentation includes a link to a respective resource, wherein the first search result in the presentation includes a link that, upon a selection by a user, can cause the first suggested query to be submitted to a search engine.

Providing result-based query suggestions

A stopword detection component detects stopwords (also stop-phrases) in search queries input to keyword-based information retrieval systems. Potential stopwords are initially identified by comparing the terms in the search query to a list of known stopwords. Context data is then retrieved based on the search query and the identified stopwords. In one implementation, the context data includes documents retrieved from a document index. In another implementation, the context data includes categories relevant to the search query. Sets of retrieved context data are compared to one another to determine if they are substantially similar. If the sets of context data are substantially similar, this fact may be used to infer that the removal of the potential stopword(s) is not material to the search. If the sets of context data are not substantially similar, the potential stopword can be considered material to the search and should not be removed from the query.

Locating meaningful stopwords or stop-phrases in keyword-based retrieval systems

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating descriptive text for images. In one aspect, a method includes identifying a set of seed descriptors for an image in a document that is hosted on a website. For each seed descriptor, structure information is generated that specifies a structure of the document with respect to the image and the seed descriptor. One or more templates are generated for each seed descriptor using the structure information for the seed descriptor. Each template can include image location information, document structure information, image feature information, and a generative rule that generates descriptive text for other images in other documents. Descriptive text for other images is generated using the templates and the other documents. The descriptive text is associated with the images.

Generating descriptive text for images in documents using seed descriptors

A system and method for generating query refinement suggestions may include collecting refinement data for at least one received source query. The collected refinement data is then clustered to form at least one cluster. At least one potential refinement query suggestion is identified from the refinement data within the at least one cluster.

Systems and methods for providing search query refinements

Methods, systems, and apparatus, including computer program products, for establishing preferences for sites that are explicitly identified by a user by either a textual input or an acceptance in response to a recommendation, and adjusting the presentation of search results based on these preferences.

Preferred sites

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a measure of quality for a resource. In one aspect, a method includes determining a seed score for each seed resource in a set. The seed score for a seed resource can be based on a number of resources that include a link to the seed resource and a number of selections of the links A set of source resources is identified. A source score is determined for each source resource. The source score for a source resource is based on the seed score for each seed resource linked to by the source resource. Source-referenced resources are identified. A resource score is determined for each source-referenced resource. The resource score for a source-referenced resource can be based on the source score for each source resource that includes a link to the source-referenced resource.

Determining a quality measure for a resource

A system and method for providing search query refinements are presented. A stored query and a stored document are associated as a logical pairing. A weight is assigned to the logical pairing. The search query is issued and a set of search documents is produced. At least one search document is matched to at least one stored document. The stored query and the assigned weight associated with the matching at least one stored document are retrieved. At least one cluster is formed based on the stored query and the assigned weight associated with the matching at least one stored document. The stored query associated with the matching at least one stored document are scored for the at least one cluster relative to at least one other cluster. At least one such scored search query is suggested as a set of query refinements.

System and method for providing search query refinements

Methods, systems, and apparatus, including computer program products, for generating synthetic queries using seed queries and structural similarity between documents are described. In one aspect, a method includes identifying embedded coding fragments (e.g., HTML tag) from a structured document and a seed query; generating one or more query templates, each query template corresponding to at least one coding fragment, the query template including a generative rule to be used in generating candidate synthetic queries; generating the candidate synthetic queries by applying the query templates to other documents that are hosted on the same web site as the document; identifying terms that match structure of the query templates as candidate synthetic queries; measuring a performance for each of the candidate synthetic queries; and designating as synthetic queries the candidate synthetic queries that have performance measurements exceeding a performance threshold.

Query generation using structural similarity between documents

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating synthetic descriptive text. One of the methods includes identifying a group of linking resources, wherein each of the linking resources includes a link to a respective target resource; determining, from a search engine index, that at least some of the target resources are associated with seed queries; generating term location information that identifies, for each seed query, locations of terms from the seed query in the linking resource that links to the target resource associated with the seed query; generating synthetic descriptive text for the target resources based on the term location information; and associating the synthetic descriptive text with the target resources in the search engine index.

Generating synthetic descriptive text

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using synthetic descriptive text to rank search results. One of the methods includes receiving a search query from a user device; receiving data identifying a plurality of search result resources and respective initial scores for each of the search result resources; determining, from a search engine index, that a particular search result resource of the plurality of search result resources is associated with one or more pieces of synthetic descriptive text, wherein each piece of synthetic descriptive text is generated by applying a respective template to a respective linking resource that links to the particular search result resource; computing a synthetic descriptive text score for the particular search result resource; and adjusting the initial score for the particular search result resource based at least in part on the synthetic descriptive text score.

Using synthetic descriptive text to rank search results

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San Francisco, CA, United States of America

Paul Haahr