Speaker
Description
The presentation will introduce a GraphRAG-based approach to research data retrieval from research data catalogues, using the Text+ Registry as an example.
Retrieval-Augmented Generation (RAG) systems have become a cornerstone for LLM-based question-answering tasks involving individual (potentially private or sensitive) unstructured data. However, traditional RAG pipelines often lack an in-depth understanding of the underlying data and the ability to retrieve contextual information from it.
GraphRAG based approaches can address this by utilizing structured data in a knowledge graph to capture deeper relational context, enabling more precise retrieval and a more nuanced understanding.
The first implementation has already shown that GraphRAG outperforms standard RAG in terms of both retrieval precision and response quality.
The presentation will also contain a system demonstration.
| I want to give a Lightning Talk | yes |
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