Abstract: Briefly, embodiments are directed to a system, method, and article for acquiring a symbol comprising a representation of input data. The symbol may be converted into an emergent language expression in an emergent language via processing of a first neural network. Transmission of the emergent language expression may be initiated over a communications network, where the emergent language comprises a language based on and specific to the input data. The emergent language expression may be translated back into the representation of the input data via processing of a second neural network.

Abstract: A system, method, and computer-readable medium to extract information from at least one of code and text documentation, the extracted information conforming to a base ontology and being extracted in the context of a knowledge graph; add the extracted information to the knowledge graph; generate, in a mixed interaction with a user selectively in communication with the system, computational models including scientific knowledge; and persist, in a memory, a record of the generated computational models.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising receiving a node group at an integration module, wherein the node group includes one or more requests for internal data and external data, wherein internal data is data stored in an internal datastore and external data is data stored outside of the internal data store; identifying in a configuration data, one or more meta-data nodes from the node group, wherein each meta-data node indicates a request for external data; retrieving the internal data via execution of an internal data query; determining an order of execution for the one or more meta-data nodes; executing a first meta-data node based on the determined order to generate a first result; ingesting the first result into a semantic datastore; and executing a query to generate a final result, wherein the query includes the retrieved internal data and the ingested first result. Numerous other aspects are provided.

Abstract: Briefly, embodiments are directed to a system, method, and article for acquiring a symbol comprising a representation of input data. The symbol may be converted into an emergent language expression in an emergent language via processing of a first neural network. Transmission of the emergent language expression may be initiated over a communications network, where the emergent language comprises a language based on and specific to the input data. The emergent language expression may be translated back into the representation of the input data via processing of a second neural network.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising receiving a node group at an integration module, wherein the node group includes one or more requests for internal data and external data, wherein internal data is data stored in an internal datastore and external data is data stored outside of the internal data store; identifying in a configuration data, one or more meta-data nodes from the node group, wherein each meta-data node indicates a request for external data; retrieving the internal data via execution of an internal data query; determining an order of execution for the one or more meta-data nodes; executing a first meta-data node based on the determined order to generate a first result; ingesting the first result into a semantic datastore; and executing a query to generate a final result, wherein the query includes the retrieved internal data and the ingested first result. Numerous other aspects are provided.

Abstract: A system, method, and computer-readable medium to extract information from at least one of code and text documentation, the extracted information conforming to a base ontology and being extracted in the context of a knowledge graph; add the extracted information to the knowledge graph; generate, in a mixed interaction with a user selectively in communication with the system, computational models including scientific knowledge; and persist, in a memory, a record of the generated computational models.

Abstract: Among other things, one or more techniques and/or systems are provided for building an action catalogue, generating an action frame for an action within the action catalogue, and/or executing an action. In an example, an action may be included within the action catalogue based upon descriptive text associated with an application indicating that the application is capable of performing the action (e.g., a movie app may be capable of performing an order movie tickets action). A parameter (e.g., a movie name) and/or an execution endpoint (e.g., a uniform resource identifier used to access movie ticket ordering functionality) may be used to generate an action frame for the action. In this way, user intent to perform an action may be identified from user input (e.g., a spoken command), and the action may be performed (e.g., on behalf of the user with minimal additional user input) by using the action frame.

Abstract: Among other things, one or more techniques and/or systems are provided for building an action catalogue, generating an action frame for an action within the action catalogue, and/or executing an action. In an example, an action may be included within the action catalogue based upon descriptive text associated with an application indicating that the application is capable of performing the action (e.g., a movie app may be capable of performing an order movie tickets action). A parameter (e.g., a movie name) and/or an execution endpoint (e.g., a uniform resource identifier used to access movie ticket ordering functionality) may be used to generate an action frame for the action. In this way, user intent to perform an action may be identified from user input (e.g., a spoken command), and the action may be performed (e.g., on behalf of the user with minimal additional user input) by using the action frame.