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Itext2kg
Overview :
iText2KG is a Python package designed to leverage large language models for extracting entities and relationships from textual documents, incrementally constructing coherent knowledge graphs. It features zero-shot capabilities, enabling knowledge extraction across various domains without specific training. The package includes modules for document distillation, entity extraction, and relationship extraction, ensuring that entities and relationships are resolved and unique. It provides a visual representation of knowledge graphs through Neo4j, supporting interactive exploration and analysis of structured data.
Target Users :
The target audience includes data scientists, researchers, and developers who need to extract information from large volumes of text data and build knowledge graphs. iText2KG automates entity and relationship extraction, reducing the need for manual data handling and enhancing both efficiency and accuracy.
Use Cases
Build knowledge graphs of scientific literature for research and data analysis.
Extract information from company websites to construct company knowledge graphs for business analysis and decision support.
Process personal resumes to build personal knowledge graphs for career development and skill assessment.
Features
Document Distiller: Processes raw documents and reconstructs them into semantic blocks based on user-defined patterns.
Incremental Entity Extractor: Extracts unique entities from semantic blocks and resolves ambiguities, ensuring each entity has a clear definition.
Incremental Relationship Extractor: Identifies relationships between extracted entities, enhancing the graph or providing more precise connections.
Graph Integration and Visualization: Integrates the extracted entities and relationships into a Neo4j database, providing visualization of the knowledge graph.
Supports zero-shot capabilities, allowing knowledge extraction without domain-specific training.
Integrates with Neo4j for easy visualization and analysis of data.
How to Use
1. Install iText2KG, ensuring that your Python environment is set up, and use pip to install.
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2. Define or upload your predefined document distillation models.
3. Use the DocumentDistiller module to distill documents and extract semantic blocks.
4. Build knowledge graphs using the iText2KG module, extracting entities and relationships.
5. Integrate the extracted entities and relationships into a Neo4j database using the GraphIntegrator module.
6. Utilize Neo4j for visualizing and interactively exploring the knowledge graph.
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