Abstract: Systems, methods, and computer-readable media for computer-assisted output validation in machine learning/artificial intelligence platforms are disclosed. An application instance includes one or more machine learning models used to generate searchable data structures based on multimodal inputs.

Abstract: Systems, methods, and computer-readable media for generating responses to natural-language queries regarding items in unstructured documents are disclosed. An application instance that includes one or more machine learning models receives, from a subscriber computing system, a query and document comprising unstructured data. Based on the unstructured data, the application instance generates a searchable data structure using a machine learning model. A query response is generated by performing a semantic search on the searchable data structure. The query response is provided to a target application.

Abstract: Systems, methods, and computer-readable media for generating responses to natural-language queries regarding items in unstructured documents are disclosed. An application instance that includes one or more machine learning models receives, from a subscriber computing system, a query and document comprising unstructured data. Based on the unstructured data, the application instance generates a searchable data structure using a machine learning model. A query response is generated by performing a semantic search on the searchable data structure. The query response is provided to a target application.

Abstract: An application instance that includes one or more machine learning models receives, from a subscriber computing system, a document comprising unstructured data. Based on the unstructured data, the application instance generates an optimized model input that includes a plurality of parsed document sections. For each parsed document section, the application instance generates an output set by performing, by a machine learning model, at least one key information extraction operation. The machine learning model transmits the output in structured form to a target application operated or hosted at least in part by a subscriber entity associated with the subscriber computing system.

Abstract: An application instance that includes one or more machine learning models receives, from a subscriber computing system, a document comprising unstructured data. Based on the unstructured data, the application instance generates an optimized model input that includes a plurality of parsed document sections. For each parsed document section, the application instance generates an output set by performing, by a machine learning model, at least one key information extraction operation. The machine learning model transmits the output in structured form to a target application operated or hosted at least in part by a subscriber entity associated with the subscriber computing system.

Abstract: RDF store database designs and efficient techniques for converting SPARQL queries to SQL queries are described that provide faster triplet access, and which can reduce the computational overhead and cost associated with storing large volumes of RDF metadata. In various embodiments RDF data can be stored in de-normalized tables tailored to provide efficient query and storage performance. The provided query conversion techniques provide reliable and efficient query performance.

Abstract: RDF store database designs and efficient techniques for converting SPARQL queries to SQL queries are described that provide faster triplet access, and which can reduce the computational overhead and cost associated with storing large volumes of RDF metadata. In various embodiments RDF data can be stored in de-normalized tables tailored to provide efficient query and storage performance. The provided query conversion techniques provide reliable and efficient query performance.

Abstract: RDF store database designs and efficient techniques for converting SPARQL queries to SQL queries are described that provide faster triplet access, and which can reduce the computational overhead and cost associated with storing large volumes of RDF metadata. In various embodiments RDF data can be stored in de-normalized tables tailored to provide efficient query and storage performance. The provided query conversion techniques provide reliable and efficient query performance.

Abstract: RDF store database designs and efficient techniques for converting SPARQL queries to SQL queries are described that provide faster triplet access, and which can reduce the computational overhead and cost associated with storing large volumes of RDF metadata. In various embodiments RDF data can be stored in de-normalized tables tailored to provide efficient query and storage performance. The provided query conversion techniques provide reliable and efficient query performance.