Abstract: A computer-implemented technique uses a machine-trained model to convert input information provided by a user into a natural language expression and a semantic vector. The semantic vector provides a distributed representation of the natural language expression. The technique then uses the generated semantic vector to find a target data record having a target semantic vector that is within a prescribed distance to the generated semantic vector. The technique then outputs at least a target natural language expression that is associated with the target data record. The target data record is produced in an offline process that converts one or more attribute values in an existing structured data record into the target semantic vector and the target natural language expression. Also described herein is a technique for training the machine-trained model and for updating existing collections of data records using the machine-trained model.

Abstract: A computer-implemented technique uses a machine-trained model to convert input information provided by a user into a natural language expression and a semantic vector. The semantic vector provides a distributed representation of the natural language expression. The technique then uses the generated semantic vector to find a target data record having a target semantic vector that is within a prescribed distance to the generated semantic vector. The technique then outputs at least a target natural language expression that is associated with the target data record. The target data record is produced in an offline process that converts one or more attribute values in an existing structured data record into the target semantic vector and the target natural language expression. Also described herein is a technique for training the machine-trained model and for updating existing collections of data records using the machine-trained model.

Abstract: A computer-implemented technique uses a machine-trained model to convert input information provided by a user into a natural language expression and a semantic vector. The semantic vector provides a distributed representation of the natural language expression. The technique then uses the generated semantic vector to find a target data record having a target semantic vector that is within a prescribed distance to the generated semantic vector. The technique then outputs at least a target natural language expression that is associated with the target data record. The target data record is produced in an offline process that converts one or more attribute values in an existing structured data record into the target semantic vector and the target natural language expression. Also described herein is a technique for training the machine-trained model and for updating existing collections of data records using the machine-trained model.

Abstract: Examples of the present disclosure describe systems and methods for returning resources from an isolated collection in response to a query. In some instances, the results of the query may return a large number of resources. As such, the returned results may be paginated so that a select number of results are returned. When a subsequent query is executed on the isolated collection, one or more cursors or skip tokens are used to ensure a subsequent pagination of results may be returned.

Abstract: A computer-implemented technique uses a machine-trained model to convert input information provided by a user into a natural language expression and a semantic vector. The semantic vector provides a distributed representation of the natural language expression. The technique then uses the generated semantic vector to find a target data record having a target semantic vector that is within a prescribed distance to the generated semantic vector. The technique then outputs at least a target natural language expression that is associated with the target data record. The target data record is produced in an offline process that converts one or more attribute values in an existing structured data record into the target semantic vector and the target natural language expression. Also described herein is a technique for training the machine-trained model and for updating existing collections of data records using the machine-trained model.

Abstract: A technique is described herein for transforming audio content into images. The technique may include: receiving the audio content from a source; converting the audio content into a temporal stream of audio features; and converting the stream of audio features into one or more images using one or more machine-trained models. The technique generates the image(s) based on recognition of: semantic information that conveys one or more semantic topics associated with the audio content; and sentiment information that conveys one or more sentiments associated with the audio content. The technique then generates an output presentation that includes the image(s), which it provides to one or more display devices for display thereat. The output presentation serves as a summary of salient semantic and sentiment-related characteristics of the audio content.

Abstract: In the present disclosure, systems, methods and devices for dynamically extending a federated graph are provided. Data may be received in a variety of unrelated forms and one or more classification types may be associated with received data. Relationships that link new data input to existing data sets and their associated classification types may be generated and new data input may be incorporated and stored with existing data sets according to generated data relationships. A data set may be sorted, and resources associated with a data set may be retrieved, based on dynamic query types.

Abstract: A technique is described herein for transforming audio content into images. The technique may include: receiving the audio content from a source; converting the audio content into a temporal stream of audio features; and converting the stream of audio features into one or more images using one or more machine-trained models. The technique generates the image(s) based on recognition of: semantic information that conveys one or more semantic topics associated with the audio content; and sentiment information that conveys one or more sentiments associated with the audio content. The technique then generates an output presentation that includes the image(s), which it provides to one or more display devices for display thereat. The output presentation serves as a summary of salient semantic and sentiment-related characteristics of the audio content.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for providing access to one or more nodes associated with a shared graphical dataset are provided. In one example, a request to access a resource associated with a shared graphical dataset may be received. A determination may be made as to whether an authorization element for the resource provides one or more access types for the resource based on the request. Access may be provided to the resource for each of the one or more access types that the authorization element is determined to provide access to. In another example, a caller application may request user permission information from a shared graphical dataset. The user permission information may be received and a token comprising one or more authorized access types that the user has for the graphical dataset may be generated and provided back to the graphical dataset.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for merging a plurality of subsets split from a first graphically represented data set are provided. A query specifying one or more relationships between a node in a first subset of the plurality of subsets and a node in a second subset of the plurality of subsets may be received. One or more outgoing edges to the second subset corresponding to at least one of the one or more relationships common to the node in the first subset and the node in the second subset may be identified. The first subset and the second subset may be merged, and feedback related to the node in the second subset corresponding to the query may be provided. According other examples, a lazy merge may be implemented in querying disjoined subsets and subsequently merging those subsets.

Abstract: Examples of the present disclosure describe systems and methods for returning resources from an isolated collection in response to a query. In some instances, the results of the query may return a large number of resources. As such, the returned results may be paginated so that a select number of results are returned. When a subsequent query is executed on the isolated collection, one or more cursors or skip tokens are used to ensure a subsequent pagination of results may be returned.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for dynamically extending a federated graph are provided. Data may be received in a variety of unrelated forms and one or more classification types may be associated with received data. Relationships that link new data input to existing data sets and their associated classification types may be generated and new data input may be incorporated and stored with existing data sets according to generated data relationships. A data set may be sorted, and resources associated with a data set may be retrieved, based on dynamic query types.