Amazon Technologies, Inc.

Arun Kumar Ravi

Parminder Bhatia

Thiruvarul Selvan Senthivel

Emine Busra Celikkaya

Arjun Mukhopadhyay

Varun Sembium Varadarajan

Jeremy Douglas Fehr

Shyam Ramaswamy

Eric Chen

Mohammed Khalilia

Fan Xiang

Tiberiu Mircea Doman

Vaibhav Lella

Ryan Hood

Borui Zhang

Sneha Avuthu

Data sets may be enhanced to create data stores. Request to create data stores may be received. As part of performing the request to create a data store, items stored in an extensible data format may be identified for machine learning enhancement. Machine learning models may be applied to generate additional data from data in the items. The additional data may be added to extend the items and store the extended items in a new data store.

Enhancing data sets to create data stores

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.

Service architecture for ontology linking of unstructured text

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.

Service architecture for entity and relationship detection in unstructured text

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Kirkland, WA, United States of America

Arun Kumar Ravi