Uni Mannheim - Business Informatics Masters Thesis - Arshad Mehmood

Public repo for my masters thesis for the chair of Data and Web science:

Identification of Polysemous Entities in a Large Scale Database (WebIsALOD)

First of all the WebIsALOD dataset should be downloaded, extracted and saved in the data folder.

1. Fix the dataset URI's: To fix the dataset URI's run the python script called fix_dataset_uris.py.

2. Extract concept documents files and save preprocessed clean files:

   To save the clean preprocessed files run the python script called Read_And_Clean.py.

3. Download Wikipedia data:

   Use the following script to download the latest Wikipedia English articles dump:

   curl –O https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2

4. Preprocess Wikipedia data using Gensim:

   To preprocess the Wikipedia data use the Gensim's script:

   python -m gensim.scripts.make_wiki

5. Train LDA model with Wikipedia data:

   wiki_wordids.txt and wiki_tfidf.mm files generated in the previous step are required by the models using Wikipedia data.

   To train the LDA models with Wikipedia data, run the python script called wiki_lda.py.

6. Train LDA model with WebIsALOD data:

   To train the LDA models with WebIsALOD data, run the python script called webisalod_lda.py.

7. Train HDP model:

   To train the LDA models with Wikipedia data, run the python script called wiki_hdp.py.

8. Classification using only topic modeling:

   To run the classification model with only topic modeling, run the python script called polysemous_words.py.

9. Classification using topic modeling and supervised machine learning algorithms:

   To run the classification model with only topic modeling, run the python script called supervised_classifier.py.