Abstract: Techniques for ontology linking of unstructured text as a service are described. A service may receive a request to link unstructured text to a standardized ontology, and the service may segment and tokenize the unstructured text and send the result to multiple services implementing multiple deep machine learning models trained to identify particular entities and one or more relationships between entities. The service may perform a search of the standardized ontology to identify a set of similar candidates from the standardized ontology for the detected entities and the one or more relationships, and then rank the set of similar candidates from the standardized ontology according to their similarity to the detected entities within the unstructured text. The output from the service may include a result identifying a highest ranked candidate of the set of similar candidates from the standardized ontology for the detected entities within the unstructured text.

Abstract: An NLQ-SQLQ tool or service of a provider network may receive a natural language query (NLQ) from a client and convert the NLQ to an SQL query using ontological codes and placeholders. For one or more portions of the NLQ, the tool/service determines that the portion is associated with one or more codes of an ontology. The tool/service then assigns, based on criteria, a particular code to the portion. The tool/service replaces portions of the NLQ with different argument placeholders to generate a modified NLQ. A trained model converts the modified NLQ into an initial SQL query that has argument placeholders and subquery placeholders. The tool/service generates a final SQL query based on the initial SQL query, predefined SQL subquery templates associated with the subquery placeholders, and codes associated with the argument placeholders. The tool/service executes the final SQL query and sends results to the client.

Abstract: An NLQ-SQLQ tool or service of a provider network may receive a natural language query (NLQ) from a client and convert the NLQ to an SQL query using ontological codes and placeholders. For one or more portions of the NLQ, the tool/service determines that the portion is associated with one or more codes of an ontology. The tool/service then assigns, based on criteria, a particular code to the portion. The tool/service replaces portions of the NLQ with different argument placeholders to generate a modified NLQ. A trained model converts the modified NLQ into an initial SQL query that has argument placeholders and subquery placeholders. The tool/service generates a final SQL query based on the initial SQL query, predefined SQL subquery templates associated with the subquery placeholders, and codes associated with the argument placeholders. The tool/service executes the final SQL query and sends results to the client.

Abstract: Techniques for ontology linking of unstructured text as a service are described. A service may receive a request to link unstructured text to a standardized ontology, and the service may segment and tokenize the unstructured text and send the result to multiple services implementing multiple deep machine learning models trained to identify particular entities and one or more relationships between entities. The service may perform a search of the standardized ontology to identify a set of similar candidates from the standardized ontology for the detected entities and the one or more relationships, and then rank the set of similar candidates from the standardized ontology according to their similarity to the detected entities within the unstructured text. The output from the service may include a result identifying a highest ranked candidate of the set of similar candidates from the standardized ontology for the detected entities within the unstructured text.

Abstract: Techniques for entity and relationship detect from unstructured text as a service are described. A service may receive a request to identify entities within a provided unstructured text element, and the service may segment and tokenize the unstructured text and send the result to multiple services implementing multiple deep machine learning models trained to identify particular entities. The service may send additional requests to an additional service or services implementing additional deep machine learning models to identify relationships between detected attributes and ones of the detected entities. The outputs from all services can be analyzed and consolidated into a single result that identifies the entities, any attributes of the entities, and confidence scores indicating the confidence in each detected entity.