TADA: TAbular Data Annotation

Get the prerequisites

1. python
2. pip
3. virtualenv

Installing the prerequisite python libraries:

1. You can run the below command to install the libraries.

pip install -r requirements.txt

Note: to run the explore\explore.py (that show the diagrams, you would need to install the latest matplotlib from github (not in pip yet) here.

Running the application:

1. Go to the folder tadacode

cd tadacode

2. If it is your first time, run the below command:

python manage.py makemigrations tadaa

3. If it is your first time, run the below command:

python manage.py migrate tadaa

4. Run the server using the below command:

python manage.py runserver

5. Go to http://127.0.0.1:8000

Note: Running the application locally, after creating the model or the prediction when the status reaches 100 (even before showing that on the screen) it stop and you would need to restart the server using the below command:

python manage.py runserver

Data for entity column semantic labeling

Olympic Data

Project Components:

Fuzzy c-means:

This includes the FCM class which is an implementation of fuzzy c-means. The implementation is inspired by the code of k-means in scikit-learn. Folder: clustering

Web app and workflow:

It contains the workflow of the application (e.g. creating the model and perform the prediction using FCM) and the web related code (e.g. WUI and the server code). Folder: tadaa Files:learning.py

data extraction

This model is responsible for extracting the data for a collection of classes from the endpoint using SPARQL query. File: data_extraction.py and easysparql.py

Step by Step guide

step by step guide

Data

Olympic Data

Report

report