Google Inc.

Vinicius J Fortuna

Andrew William Hogue

Daniel Loreto

Jeffrey C Reynar

Engin Cinar Sahin

John R Provine

Ankur Bhargava

Andriy Bihun

Elena Erbiceanu

Emma Sarah Persky

Randolph Gregory Brown

Leonardo A Laroco, Jr

Kevin Lerman

Benjamin J Mann

John J Lee

A server receives a query identifying one or more attributes of an entity. The server accesses a set of candidate templates for answering the query based on the one or more attributes of the entity, each candidate template having one or more fields, wherein each field is associated with at least one constraint. The server obtains a set of information that answers the query and selects a template from the set of candidate templates, where the selected template has a maximum number of fields with constraints that are satisfied by the set of information. The server then generates a phrase by adding the set of information to the one or more fields of the selected template, such that the phrase comprises an answer to the query. Finally, the server communicates the phrase to a client device.

Generating query answers

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for query analysis. Queries are identified in query data, and an entity-descriptive portion and a suffix are determined in each query. Query counts are determined for a number of times that the respective queries occur in the query data. Based on the query counts, an entity-level count is estimated, which represents a number of query submissions that include the particular suffix and are considered to refer to a first entity. The entity is determined to be a particular type of entity. A type-level count is determined, which represents a number of query submissions that include the first suffix and are estimated to refer to entities of the particular type of entity. A score is assigned to the particular suffix based on the entity-level count and the type-level count.

Identifying and ranking attributes of entities

Methods, systems, and apparatus, including computer programs stored on computer storage media, for populating a structured presentation with new values. One aspect can be embodied in machine-implemented methods that include the actions of obtaining a plurality of instances and a plurality of attributes; for each instance: identifying one or more documents from an unstructured document collection that are relevant to the instance, where each of the one or more documents include at least a value for an attribute in the plurality of attributes; and establishing a subset of the one or more values as characterizing the instance; and adding each instance, the respective attributes, and the respective subset of values to a structured data collection.

Populating a structured presentation with new values

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for information retrieval. In one aspect, a method includes receiving a fact query; determining an expected type of answer to the fact query; identifying search results responsive to the fact query; identifying phrases from the resources corresponding to the search results that correspond to a form of the expected type; determining a score for each of the identified phrases; and presenting an answer to the fact query using the scores.

Returning factual answers in response to queries

Methods, systems, and apparatus, including computer programs stored on computer storage media, for retrieval and display of information from an electronic document collection. One aspect can be embodied in machine-implemented methods that include the actions of receiving description data describing a preexisting structured presentation, comparing characteristics of the preexisting structured presentation with content of electronic documents in an unstructured collection of electronic documents to locate electronic documents that identify a new attribute that is relevant to the preexisting structured presentation, adding an identifier of the new attribute to the preexisting structured presentation to form an expanded structured presentation, and outputting instructions for presenting the expanded structured presentation.

Adding new attributes to a structured presentation

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for machine learning. In one aspect, a method includes receiving a collection of facts, each fact represented as an entity-attribute-value tuple; identifying expected values for one or more individual attributes, where the identifying expected values includes, for each particular attribute: identifying facts having the attribute, calculating a value score for facts of the collection of facts having the particular attribute for each particular value, calculating a global score for all facts of the collection having the attribute, and comparing the value score to the global score such that a value is identified as an expected value if the comparison satisfies a specified threshold.

Learning expected values for facts

Methods, systems, and apparatus, including computer programs stored on computer storage media, for retrieval and display of information from an electronic document collection. In one aspect, a machine-implemented method includes the actions of receiving description data describing a preexisting structured presentation, comparing characteristics of the preexisting structured presentation with content of electronic documents in an unstructured collection of electronic documents to locate electronic documents that identify a new instance that is relevant to the preexisting structured presentation, adding an identifier of the new instance to the preexisting structured presentation to form an expanded structured presentation, and outputting instructions for presenting the expanded structured presentation.

Adding new instances to a structured presentation

Methods, systems, and apparatus, including computer programs stored on computer storage media, for populating a structured presentation with new values. One aspect can be embodied in machine-implemented methods that include the actions of receiving description data describing a preexisting structured presentation, comparing characteristics of the preexisting structured presentation with content of electronic documents in an unstructured collection of electronic documents to locate electronic documents that identify a new value that is relevant to the preexisting structured presentation, adding the new value to the preexisting structured presentation to form a new structured presentation, and outputting instructions for visually presenting the new structured presentation.

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New York, NY, United States of America

Vinicius J Fortuna