Metadata-Version: 2.1
Name: kml-analysis-parasKumarSahu
Version: 0.0.10
Summary: Wikipedia Analysis Toolkit
Home-page: https://github.com/parasKumarSahu/Knolml-Analysis-Package
Author: Paras Kumar
Author-email: paraskumardavhehal1@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# Knolml-Analysis-Package
The aim of this project is to do various types of analysis on knolml which can be used by a reseracher who is working on wikipedia data.

## Analysis1: Controversy Analysis using wiki-links
To measure the relative controversy level of various wiki-links present in a wikipedia article.

Input Format: python3 script_name input_file_name

Example: python3 controversy_analysis.py 2006_Westchester_County_torna.knolml

## Analysis2: Contributions of an author over a given period of time in a wikipedia article
To find contributions of an author in terms of words, sentences, bytes etc over a given period of time (given starting and ending dates)

Input Format: python3 script_name input_file_name start_date(YYYY-MM-DD) end_date(YYYY-MM-DD) --flag(sentences/bytes/wikilinks/words)

Example: python3 author_contribution.py 2006_Westchester_County_torna.knolml 2000-01-01 2010-01-01 --bytes

## Analysis3: Ranking all the authors based on their contribution to a given paragraph
To rank all the authors of a wikipedia article based on their contribution to a particular paragraph present in the article. The paragraph will be given as input to the program.

Input Format: python3 script_name input_file_name

Example: python3 rank_authors_based_on_para_contr.py 2006_Westchester_County_torna.knolml

## Analysis4: Finding knowledge gaps in a wikipedia article
A wikipedia article represents knowledge about some related topics, like a wikipedia article on IIT Ropar may be talking about placements of IIT Ropar in a particular section. But, in this section there was no information regarding a new branch say Biotechnology which was newly introduced. So, can we write a Python program that can tell that the information regarding placements of Biotechnology is missing from the IIT Ropar wikipedia page? Or in general can we tell that there is a knowledge gap in a wikipedia article?

Steps to find external knowledge gaps:-

1. Select a book from books folder as input file for segmentation and run python3 start_segmentation.py books/[book_name]
2. Segments would be written in segmentaion_result.csv file
3. Now we will do external segmentation using segmentaion_result.csv, run python3 find_external_gaps.py
3. You can find the External Knowledge gaps in external_gaps.txt file

Steps to train word2vec (Optional):-
1. You are already provided with a trained word2vec (wrdvecs-text8.bin), you have to delete it first
2. Once the trained model is deleted, supply a coprus with name text8 and simply run the code


