{"id":24151259,"url":"https://github.com/arshad115/uni-mannheim-masters-thesis","last_synced_at":"2026-05-08T23:04:29.560Z","repository":{"id":39837843,"uuid":"276180047","full_name":"arshad115/Uni-Mannheim-Masters-Thesis","owner":"arshad115","description":"Public repo for my masters thesis \"Identification of Polysemous Entities in a Large Scale Database (WebIsALOD)\" for University of Mannheim Masters in Business Informatics, Chair of Data and Web Science.","archived":false,"fork":false,"pushed_at":"2023-02-08T00:45:17.000Z","size":78228,"stargazers_count":1,"open_issues_count":5,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-12T09:14:50.965Z","etag":null,"topics":["classification","classification-algorithm","data-science","gensim","hdp","latent-dirichlet-allocation","lda","lda-models","masters-thesis","polysemous-entities","polysemy","thesis","topic-modeling","uni-mannheim","webisadb","wikipedia-data"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arshad115.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-06-30T18:36:26.000Z","updated_at":"2023-04-29T09:56:48.000Z","dependencies_parsed_at":"2022-08-27T19:22:24.847Z","dependency_job_id":null,"html_url":"https://github.com/arshad115/Uni-Mannheim-Masters-Thesis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arshad115%2FUni-Mannheim-Masters-Thesis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arshad115%2FUni-Mannheim-Masters-Thesis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arshad115%2FUni-Mannheim-Masters-Thesis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arshad115%2FUni-Mannheim-Masters-Thesis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arshad115","download_url":"https://codeload.github.com/arshad115/Uni-Mannheim-Masters-Thesis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241404449,"owners_count":19957658,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","classification-algorithm","data-science","gensim","hdp","latent-dirichlet-allocation","lda","lda-models","masters-thesis","polysemous-entities","polysemy","thesis","topic-modeling","uni-mannheim","webisadb","wikipedia-data"],"created_at":"2025-01-12T09:14:57.938Z","updated_at":"2026-05-08T23:04:24.538Z","avatar_url":"https://github.com/arshad115.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Uni Mannheim - Business Informatics Masters Thesis - Arshad Mehmood\nPublic repo for my masters thesis for the chair of Data and Web science:\n\n### Identification of Polysemous Entities in a Large Scale Database (WebIsALOD)\n\nFirst of all the [WebIsALOD](http://data.dws.informatik.uni-mannheim.de/webisa/webisalod-instances.nq.gz) dataset should be downloaded, extracted and saved in the `data` folder.\n\n1. Fix the dataset URI's:\n     To fix the dataset URI's run the python script called `fix_dataset_uris.py`.\n\n2. Extract concept documents files and save preprocessed clean files:\n\n   To save the clean preprocessed files run the python script called `Read_And_Clean.py`.\n\n3. Download Wikipedia data:\n\n   Use the following script to download the latest Wikipedia English articles dump:\n\n   ```bash\n   curl –O https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2\n   ```\n\n4. Preprocess Wikipedia data using [Gensim](\u003chttps://radimrehurek.com/gensim/index.html\u003e):\n\n   To preprocess the Wikipedia data use the [Gensim](\u003chttps://radimrehurek.com/gensim/index.html\u003e)'s script: \n\n   ```bash\n   python -m gensim.scripts.make_wiki\n   ```\n\n5. Train LDA model with Wikipedia data:\n\n   `wiki_wordids.txt` and `wiki_tfidf.mm` files generated in the previous step are required by the models using Wikipedia data.\n\n   To train the LDA models with Wikipedia data, run the python script called `wiki_lda.py`.\n\n6. Train LDA model with [WebIsALOD](http://data.dws.informatik.uni-mannheim.de/webisa/webisalod-instances.nq.gz) data:\n\n     To train the LDA models with [WebIsALOD](http://data.dws.informatik.uni-mannheim.de/webisa/webisalod-instances.nq.gz) data, run the python script called `webisalod_lda.py`.\n\n7. Train HDP model:\n\n     To train the LDA models with Wikipedia data, run the python script called `wiki_hdp.py`.\n\n8. Classification using only topic modeling:\n\n     To run the classification model with only topic modeling, run the python script called `polysemous_words.py`.\n\n9. Classification using topic modeling and supervised machine learning algorithms:\n\n     To run the classification model with only topic modeling, run the python script called `supervised_classifier.py`.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farshad115%2Funi-mannheim-masters-thesis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farshad115%2Funi-mannheim-masters-thesis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farshad115%2Funi-mannheim-masters-thesis/lists"}