An analysis system connects to a set of data sources and perform natural language questions based on the data sources. The analysis system connects with the data sources and retrieves metadata describing data assets stored in each data source. The analysis system generates an execution plan for the natural language question. The analysis system finds data assets that match the received question based on the metadata. The analysis system ranks the data assets and presents the ranked data assets to users for allowing users to modify the execution plan. The analysis system may use execution plans of previously stored questions for executing new questions. The analysis system supports selective preprocessing of data to increase the data quality.

Automatically mapping unlabeled input data onto a target schema via semantic type detection is described. The input data includes data elements that are structured as 2D table rows and columns forming cells. Each data element is included in a cell. The target schema includes a set of fields. Schema mapping includes mapping each column to one or more fields. More particularly, the fields are clustered into field clusters, where each field cluster includes one or more of the fields. Each column is automatically mapped to one of the field clusters of the set of field clusters. The mapping between schema fields and data columns is automatically performed based on appropriate pairings of the detected semantic types, where the semantic types are encoded in vector representations of the fields, the field clusters, and the data elements.

Introduced here are various embodiments for selectively assigning a query to an expert. A network-accessible server system may receive a query from a client device indicating a question or project proposal. The query text may be parsed and attributes of the query may be determined by inspecting the parsed query text. The query attributes may be compared with attributes associated with a pool of experts with various specialties and expertise in various fields. The network-accessible server system may match the query attributes with attributes associated with a first expert with a similarity that exceeds a threshold similarity level to identify that an expertise of the first expert matches the requested expertise in the query. The first expert may be assigned to the query and prompted to provide a response to the query.

Introduced here are various embodiments for selectively assigning a query to an expert. A network-accessible server system may receive a query from a client device indicating a question or project proposal. The query text may be parsed and attributes of the query may be determined by inspecting the parsed query text. The query attributes may be compared with attributes associated with a pool of experts with various specialties and expertise in various fields. The network-accessible server system may match the query attributes with attributes associated with a first expert with a similarity that exceeds a threshold similarity level to identify that an expertise of the first expert matches the requested expertise in the query. The first expert may be assigned to the query and prompted to provide a response to the query.

Systems and methods are described for providing contextual search results. The system may receive a search query during presentation of a video. If the query is ambiguous, the system accesses some of the frames of the video. The frames are analyzed to identify a performed action depicted in the frames. The system retrieves a keyword related to the identified action.

The ambiguous query is augmented with the keyword. The augmented search query is used to search for and output relevant search results.

A method, system, and medium for automated analysis of business intelligence each: receive natural language input from a user; evaluate, via a natural language understanding processor that includes a parser and an interpreter, the natural language input to determine an intent of the user; determine the intent of the user and generate a query based on a context manager; send an identification of the failure to a failure analysis system for human intervened analysis and refinement of a natural language model used by the natural language understand processor; assess, via a context manager processor, to determine a user interest in one or more portions of results of the query, a scrolling of the user through the results of the query; and refine, based on the user interest in the one or more portions of the results of the query, an output of the results of the query.

A system and a method for digital proof generation are provided. The system includes a data management module to manage dataset having plurality of data rows and a query execution and verification module including a commitment storage sub-module, a query execution sub-module and a result verification sub-module. The data management module receives query from the query execution sub-module, related to operation on data rows of plurality of data rows, which is processed to generate execution result associated with data rows. The execution result is transferred along with data rows to the query execution sub-module. A set of commitments associated with execution result is transferred to the commitment storage sub-module. The query execution sub-module transfers set of commitments, from the commitment storage sub-module, and data rows to the result verification sub-module for verification, and receives a verification result from the result verification sub-module.

Methods, systems, and computer-readable media for query language interoperability in a graph database are disclosed. Data elements are inserted into a graph database using one or more of a plurality of graph database query languages. The graph database query languages comprise a first graph database query language associated with a first data model and a second graph database query language associated with a second data model. The data elements are stored in the graph database using an internal data model that differs from the first and second data models. One or more of the data elements are retrieved from the graph database based at least in part on a query. The query is expressed using a different graph database query language than the graph database query language used to insert the one or more retrieved data elements.

Methods, systems, and computer-readable media for query language interoperability in a graph database are disclosed. Data elements are inserted into a graph database using one or more of a plurality of graph database query languages. The graph database query languages comprise a first graph database query language associated with a first data model and a second graph database query language associated with a second data model. The data elements are stored in the graph database using an internal data model that differs from the first and second data models. One or more of the data elements are retrieved from the graph database based at least in part on a query. The query is expressed using a different graph database query language than the graph database query language used to insert the one or more retrieved data elements.

A method and an apparatus for processing an intelligent voice query. A voice query input is received from a user. Automatic speech recognition and natural language understanding generate structured query data. It is modified based on an input adaptation rule to obtain modified structured query data appropriate for a content providing server, which provides a query result output corresponding to the modified structured query data. Input adaptation rules may comprise rule sets based on behavior patterns of the user and/or business recommendations. The query result output can be used for natural language generation, which may have similar adaptation rules for output.