Salesforce, Inc.

Matthieu Michel Robin Landos

Marc Brette

Guillaume Jean Mathieu Kempf

Darya Brazouskaya

Qianqian Shi

Georgios Balikas

Mario Sergio Rodriguez

Francisco Borges

Arvind Srikantan

Francisco Dellatorre Borges

Arieta Shabani

A method for managing features for a search system using declarative metadata. The method includes receiving search metadata including declarative statements identifying at least one search feature to be enabled across a plurality of components of the search system, performing functional verification of the at least one search feature, testing the at least one search feature, and enabling the at least one search feature in at least one of the plurality of components of the search system in response to positive functional verification and positive testing.

Management of search features via declarative metadata

A database system may receive a natural language query that is associated with a tenant of a multi-tenant system. The natural language query may be parsed into a set of tokens, and the set of tokens may be tagged, using a tenant specific tagging model associated with the tenant, the set of tokens with at least one pre-configured data type identifier that is configured for the plurality of tenants. A global tagging model that supports the plurality of tenants of the multi-tenant system may tag the set of tokens with at least one category identifier. The global tagging model may use the natural language query and the pre-configured data type identifier to identify the at least one category identifier. The system may execute a database query on a database associated with the tenant using the at least one pre-configured data type identifier and the at least one category identifier.

Tenant specific and global pretagging for natural language queries

A method and apparatus for generating personalized suggestions for natural language search queries, where the method includes receiving a natural language query input from a user, obtaining set of suggestions for the natural language query, identifying a set of concepts in the set of suggestions, applying co-occurrence model to the set of concepts to obtain user specific concept probabilities and vocabulary probabilities, computing suggestion scores for at least one suggestion in the set of suggestions based on the user specific concept probabilities and vocabulary probabilities, and ranking the set of suggestions based on suggestion scores.

Personalized NLS query suggestions using language models

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Grenoble, France

Matthieu Michel Robin Landos