This repository provides a pipeline to create a knowledge graph from raw texts. The pipeline concatenate major steps including:

• Data processing: transform labeled text data to the Subject-Predicate-Object (SPO) format
• Training: use a RNN-based algorithm to train an AI model to predict SPOs from given texts
• Create a Neptune database: if the training metric (F1-Score) passes the threshold, create a Neptune database
• Batch Transform: use the model trained in the Training step to do inferences on the test data
• Bulk load: transform the inference results to the format which can be recognized by the bulkload function of Neptune, and load the transformed data to the Neptune database.

• Create an AWS account or use an existing AWS account.
• Create a SageMaker Notebook instance. When you set up the notebook instance, you need to pay attention to following configurations:
  1. IAM role: you should attach policies of AmazonSageMakerFullAccess, IAMFullAccess, AmazonS3FullAccess, AmazonSNSFullAccess and NeptuneFullAccess to the IAM role.
  2. Network: in order to access the Neptune database created in the pipeline, a VPC is required to run the notebook.

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.