Metadata-Version: 2.1
Name: kml-analysis-parasKumarSahu
Version: 0.0.19
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. This pip package supports Python3.

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

Module: from kml_analysis_pkg import controversy

Input Format: controversy.run(input_file_name)

Example: controversy.run("sample.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)

Module: from kml_analysis_pkg import author_contribution

Input Format: author_contribution.run("sample.knolml", start_date, end_date, measure_option)

Date Format: YYYY-MM-DD

Measure Options: sentences/bytes/wikilinks/words

Example: author_contribution.run("sample.knolml", "2000-01-01", "2010-01-01", "words")

## 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.

Module: from kml_analysis_pkg import author_para_contribution

Input Format: author_para_contribution.run(input_file_name)

Example: author_para_contribution.run("sample.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:-

Module: from kml_analysis_pkg import external_gaps

Input Format: external_gaps.run(input_file_name, word_vector_model)

Word Vector Model: Pretrained model avaiable at https://github.com/parasKumarSahu/Knolml-Analysis/blob/master/Text-Segmentation/wrdvecs-text8.bin

Example: external_gaps.run("GeneralScience.txt", "wrdvecs-text8.bin")


