Graph-Augmented Legal RAG
What it is
A research project that builds a knowledge graph from unstructured legal text to enhance Retrieval-Augmented Generation (RAG). It uses network theory and NLP to map legal concepts and provide context-rich responses from LLMs.
The problem it solves
Standard RAG systems often struggle with complex, interconnected legal concepts that require understanding relationships rather than just semantic similarity.
What you could use it for
- Extracting and visualizing complex legal concepts from German legal texts.
- Improving the accuracy of legal document Q&A systems by providing graph-based context.
- Analyzing relationships between legal entities and concepts using network graph analysis.
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