Graphusion
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Graphusion
Overview :
Graphusion is a pipeline tool designed for extracting knowledge graph triples from text. It builds knowledge graphs through a series of steps, including concept extraction, candidate triple extraction, and triple fusion. This tool is significant as it automates the extraction of structured information from large volumes of text data, supporting knowledge management and data science projects. The main advantages of Graphusion include its automation capabilities, adaptability to different datasets, and flexible configuration options. Developed by tdurieux, the related code and documentation can be found on GitHub. Currently, the tool is free, but the pricing strategy may change based on developer updates and maintenance.
Target Users :
The target audience of Graphusion includes data scientists, researchers, and developers, especially those who need to extract structured information from text data to build knowledge graphs. This tool is suitable for them as it provides an automated solution to process and analyze large volumes of text data, saving time and resources while increasing efficiency.
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Use Cases
Researchers use Graphusion to extract key concepts and relationships from academic papers, constructing knowledge graphs for their respective fields.
Businesses utilize Graphusion to analyze customer feedback, extracting critical information for product improvements.
Developers use Graphusion to extract terms and definitions from technical documents, creating a technical knowledge base.
Features
Create a new conda environment and install required packages.
Process text files from a specified directory as input.
Requires a JSON file to define relationships.
Provides a preprocessing notebook to convert data formats.
Run the entire pipeline via command line.
Outputs include concept abstraction, extracted triples, and fused triples.
Supports optimization of results through parameter adjustments.
Offers detailed usage instructions and parameter configurations.
How to Use
1. Create and activate a new conda environment.
2. Install the dependencies listed in requirements.txt using pip.
3. Prepare the input text file and a JSON file that defines the relationships.
4. Use the preprocess.ipynb notebook to convert the data into the required format.
5. Run main.py from the command line, specifying necessary parameters such as dataset name and relationship definition file path.
6. Adjust other parameters as needed, such as model name and maximum response token count.
7. Run the pipeline and check the output files, which include concept abstraction, extracted triples, and fused triples.
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