At reseachkernel we are building knowldge graph storing in a graph database, we use doc2vec by gensim for getting similarity between research papers. Keyphrase Extraction for extracting key words from research papers, and summarizing researhing papers. All these extracted data will be stored into knowldge graph that can be used for multi disciplinary research.
├── README.md
├── data
│ ├── daily_update
│ ├── pdf
│ ├── references
│ ├── tar
│ └── text
├── main.py
├── requirements.txt
└── rk_brain
├── Utils
│ ├── __init__.py
│ ├── clean_metadata.py
│ └── credentials.py
├── __init__.py
├── arxiv_db_updater
│ ├── __init__.py
│ ├── download_pdf.py
│ ├── get_s3.py
│ ├── get_tarfile.py
│ ├── pdf_metadata_fetcher.py
│ └── rss_fetcher.py
├── contentbased_recsys
│ └── scripts
│ ├── clean_metadata.py
│ ├── database_upload.py
│ ├── online-tran.py
│ ├── online_train.py
│ └── train.py
├── etl
│ ├── __init__.py
│ └── pdftotext.py
└── knowldgegraph
├── abstract
│ ├── neo4j_node_builder_abstract.py
│ └── neo4j_relationship_creator_abstract.py
├── neo4j_abstract.py
├── neo4j_node_builder.py
├── neo4j_relationship_creator.py
└── update_neo4j.py
Project Structure is fairly simple and self explanatory. For understanding checkout the bellow ETL and traning graph.
We need contribution on Finding best ETL process for Doc2Vec model. Also, need contribution on developing a Keyphrase Extraction model.
