centre-for-humanities-computing / Semantic-Kernel

Tool for building and visuaulizing neural concept graphs

Semantic Kernel - Visualization of Neural Concept Graphs

Semantic kernel trains neural embeddings of a plain text data set either as vanilla texts or tabular data, and generate a conceptual graph based on a query list. The graph is hierarchical such that the first level consists of the $m$ strongest associated terms with the query list (displayed in caps), and the second level consists of the $n$ strongest associated terms with the first level.

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

For running in virtual environment (recommended) and assuming python3.6+ is installed.

sudo pip3 install virtualenv
virtualenv -p /usr/bin/python3.6 nuke
source nuke/bin/activate

Installing

Clone repository and install requirements

git clone https://github.com/centre-for-humanities-computing/Semantic-Kernel.git
pip install requirements.txt

To run train model and generate graph

./main.sh

Running the tests

Explain how to run the automated tests for this system

Break down into end to end tests

test that neural embeddings are trained by semantic_vect

./test.sh

And coding style tests

Explain what these tests test and why

Give an example

Deployment

Add additional notes about how to deploy this on a live system

Built With

Contributing

Versioning

Authors

Kristoffer L. Nielbo

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments