Amazon Technologies, Inc.

Luyang Kong

Parminder Bhatia

Meng Wang

Jasmeet Chhabra

Tim Januschowski

Laurent Callot

Siddhi Pathak

Lifan Chen

Andrey Kan

Christoper Winestock

Ming Chen

Baris Kurt

Pramuditha Perera

Mostafa Rahmani

Techniques for performing anomaly detection are described. An exemplary method includes receiving a request to detect potential anomalies using an anomaly detection system having at least one anomaly scoring model; processing the received data using the anomaly detection system to score the data to determine when the data is potentially anomalous based on one or more thresholds; requesting feedback of at least one determined potential anomaly; receiving feedback on the least one determined potential anomaly; and adjusting at least one of one or more of thresholds used to determine potential anomalies and what is considered an anomaly without adjusting the at least one anomaly scoring model.

Anomaly detection using feedback

Techniques for generating a dataset from a knowledge graph are described. An exemplary method includes receiving a request to generate a dataset from a knowledge graph to be stored in the storage; generating a dataset comprising a plurality of mention-concept pairs from the knowledge graph according to the request based one or more of a synonym-based and graph-based evaluation of the knowledge graph and a custom ontology for the knowledge graph; and storing the generated dataset in the storage.

Dataset generation from a knowledge graph using a custom ontology

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Seattle, WA, United States of America

Luyang Kong