Google Inc.

Rahul Gupta

John Blitzer

Fernando Pereira

Dekang Lin

Jakob David Uszkoreit

Engin Cinar Sahin

Ciprian Ioan Chelba

Amarnag Subramanya

Alon Yitzchak Halevy

Ni Lao

Steven Euijong Whang

Evgeniy Gabrilovich

Fei Wu

Shaohua Sun

Mohamed Yahya

Implementations include systems and methods for querying a data graph. An example method includes receiving a machine learning module trained to produce a model with multiple features for a query, each feature representing a path in a data graph. The method also includes receiving a search query that includes a first search term, mapping the search query to the query, and mapping the first search term to a first entity in the data graph. The method may also include identifying a second entity in the data graph using the first entity and at least one of the multiple weighted features, and providing information relating to the second entity in a response to the search query. Some implementations may also include training the machine learning module by, for example, generating positive and negative training examples from an answer to a query.

Querying a data graph using natural language queries

Methods and systems are provided for a question answering. In some implementations, a data element to be updated is identified in a knowledge graph and a query is generated based at least in part on the data element. The query is provided to a query processing engine. Information is received from the query processing engine in response to the query. The knowledge graph is updated based at least in part on the received information.

Question answering to populate knowledge base

A language processing system collects similar queries and respective responses and aggregated by responses. Incorrect responses are determined and filtered by the aggregation. The remaining responses are then used to query a high precision system for attributes of entities specified by the queries. The attribute type is determined from the responses of the high precision system, and corresponding parse rules are generated. The parse rules are then associated with an operation that yields a response that specifies an attribute of the attribute type.

Inducing command inputs from high precision and high recall data

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an ontology of entity attributes. One of the methods includes extracting a plurality of attributes based upon a plurality of queries; and constructing an ontology based upon the plurality of attributes and a plurality of entity classes.

Identifying entity attributes

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for extracting facts from a collection of documents. One of the methods includes obtaining a plurality of seed facts; generating a plurality of patterns from the seed facts, wherein each of the plurality of patterns is a dependency pattern generated from a dependency parse; applying the patterns to documents in a collection of documents to extract a plurality of candidate additional facts from the collection of documents; and selecting one or more additional facts from the plurality of candidate additional facts.

Extracting facts from documents

A method identifies pairs of first and second command inputs from respective user device sessions for which the first and second operation data are indicative of a first operation failure and a second operation success. The first operation data indicate a first operation performed on data from a first resource property in response to the first command input, and the second operation data indicate a second operation performed on data from a second resource property in response to the second command input. They system determines, from the identified pairs of first and second command inputs, command inputs for which a parsing rule that is associated with the second operation is to be generated.

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Mountain View, CA, United States of America

Rahul Gupta