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Inspired by [TextGrapher](https://github.com/liuhuanyong/TextGrapher).\n\n# Project Introduction\n\nHow to represent a text in a simple way is a chanllenge topic. This project try to extraction key information from the text by NLP methods, which contain NER extraction, relation detection, keywords extraction, frequencies words extraction. And finally show the key information in a graph way.please read the blog for more in depth details. https://fir-speedboat-5ee.notion.site/Building-Knowledge-Graphs-Using-Python-82276798233c45e8a85280e4a9308a5c?pvs=25\n\n \n1) ![flow](flow.png)\n\n# Example Demo\n\n1) ![image1](image_03.jpg)\n\n1) ![image1](image_05.jpg)\n\n2) \n![image02](grap02.png)\n# Node coloring\n- Red:Location\u003cbr\u003e\n- Blue:Person\u003cbr\u003e\n- Green:organization\u003cbr\u003e  \n- Grey:other\n# usage\n`pip install -r requirements.txt`\n\ninstall the en_core_web_lg before running the scripts \n`python -m spacy download en_core_web_lg`\n1. **Run `main.py`**: This script will generate the `graph_data.json` file.\n2. **Run `main_kg.py`**: This script will generate the `graph_data_kg.json` file.\n3. **Run `difference.py`**: This script will compute the difference between the generated files.\n4. **Run `find_ner.py`**: This script will filter the data based on Named Entity Recognition (NER).\n5. **Run `ner_plot.py`**: This script will generate the HTML file for graph visualization .\n## Switch Branch and Review Interconnections Between Documents\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkernel-loophole%2Fkg-graph","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkernel-loophole%2Fkg-graph","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkernel-loophole%2Fkg-graph/lists"}