Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

Overview

Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

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.

Prerequisites

  • 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.

Security

See CONTRIBUTING for more information.

License

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

You might also like...
Code repo for
Code repo for "RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network" (Machine Learning and the Physical Sciences workshop in NeurIPS 2021).

RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network An official PyTorch implementation of the RBSRICNN network as desc

Artificial intelligence technology inferring issues and logically supporting facts from raw text
Artificial intelligence technology inferring issues and logically supporting facts from raw text

개요 비정형 텍스트를 학습하여 쟁점별 사실과 논리적 근거 추론이 가능한 인공지능 원천기술 Artificial intelligence techno

[IJCAI-2021] A benchmark of data-free knowledge distillation from paper
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

DataFree A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation" Authors: Gongfa

TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

Source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network
Source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network

KaGRMN-DSG_ABSA This repository contains the PyTorch source Code for our paper: Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated

An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results

EasyDatas An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results Installation pip install git+https

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-based API design, PyKale enforces standardization and minimalism, via reusing existing resources, reducing repetitions and redundancy, and recycling learning models across areas.

Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

ood-text-emnlp Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them" Files fine_tune.py is used to finetune the GPT-2 mo

Generate images from texts. In Russian. In PaddlePaddle
Generate images from texts. In Russian. In PaddlePaddle

ruDALL-E PaddlePaddle ruDALL-E in PaddlePaddle. Install: pip install rudalle_paddle==0.0.1rc1 Run with free v100 on AI Studio. Original Pytorch versi

Owner
AWS Samples
AWS Samples
Hough Transform and Hough Line Transform Using OpenCV

Hough transform is a feature extraction method for detecting simple shapes such as circles, lines, etc in an image. Hough Transform and Hough Line Transform is implemented in OpenCV with two methods; the Standard Hough Transform and the Probabilistic Hough Line Transform.

Happy  N. Monday 3 Feb 15, 2022
Build a medical knowledge graph based on Unified Language Medical System (UMLS)

UMLS-Graph Build a medical knowledge graph based on Unified Language Medical System (UMLS) Requisite Install MySQL Server 5.6 and import UMLS data int

Donghua Chen 6 Dec 25, 2022
A PoC Corporation Relationship Knowledge Graph System on top of Nebula Graph.

Corp-Rel is a PoC of Corpartion Relationship Knowledge Graph System. It's built on top of the Open Source Graph Database: Nebula Graph with a dataset

Wey Gu 20 Dec 11, 2022
MLOps will help you to understand how to build a Continuous Integration and Continuous Delivery pipeline for an ML/AI project.

page_type languages products description sample python azure azure-machine-learning-service azure-devops Code which demonstrates how to set up and ope

null 1 Nov 1, 2021
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations

??A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily install with pip.

DefTruth 142 Dec 25, 2022
Point Cloud Denoising input segmentation output raw point-cloud valid/clear fog rain de-noised Abstract Lidar sensors are frequently used in environme

Point Cloud Denoising input segmentation output raw point-cloud valid/clear fog rain de-noised Abstract Lidar sensors are frequently used in environme

null 75 Nov 24, 2022
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference

RawVSR This repo contains the official codes for our paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference Xiaoh

Xiaohong Liu 23 Oct 8, 2022
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)

Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021) PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurat

Zhilu Zhang 53 Dec 20, 2022
This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEECH" submitted to ICASSP 2022

CPC_DeepCluster This is the implementation of "SELF SUPERVISED REPRESENTATION LEARNING WITH DEEP CLUSTERING FOR ACOUSTIC UNIT DISCOVERY FROM RAW SPEEC

LEAP Lab 2 Sep 15, 2022