Natural Language Processing Semantically Linking Text-Based Articles
Using Machine Learning Models you process the text. You used the entities and classes extracted to enrich your knowledge Graph. Semantically linking the documents with those extracted entities allows you to quickly find relevant articles.
- Using scispaCy for Named-Entity Recognition (Part 1)
- Linking Documents in a Semantic Graph (Part 2)
- Graph Query Searches (Part 3)
- Developing a Dynamic Author Search of Covid-19 Articles using Plotly Dash & TigerGraph (Part 4)
All the files for this demo are included here
Machine Learning TigerLab
This notebook walks through the process of setting up a cloud instance to be used for your Machine Learning experience. The Lab also does a walkthrough of incorporating TensorFlow and GCN’s with features extracted from TigerGraph:
- Lab 1 - Provision a Graph Playground
- Lab 2 - Getting Familiar with GraphStudio
- Lab 2.5 - Create a Secret and Generate a Token
- Lab 3 - Create and Execute aTensorFlow Model using Graph Features
- Lab 4 - Create and Execute a Graph Convolution Neural-Net
All the files for this demo are included here
Blogs
Predicting Initial Public Offerings Using Graph Convolutional Neural Networks