Building Knowledge Graphs on unstructured data using NLP libraries

Discover how to turn messy, unstructured text into structured knowledge that supercharges your LLM apps. In this session, we’ll explore how to extract entities and relationships from raw text using popular NLP libraries, then organize that data into a queryable knowledge graph using SurrealDB.

We’ll show how to layer in semantic search with vector embeddings—allowing your RAG system to surface the most relevant context for any user query. You’ll see a side-by-side comparison of LLM outputs with and without this approach, highlighting the value of combining graph and vector-based retrieval.

This talk will guide you through:

• Using NLP techniques to extract a knowledge graph from a large corpus.
• Storing the knowledge graph in SurrealDB.
• Enhancing retrieval by combining semantic search via vector embeddings and the knowledge graph to deliver deeper, more relevant LLM responses.

This session is ideal for developers looking to deepen their understanding of graph-based RAG, NLP for graph extraction, and RAG techniques beyond prompt engineering.

Agenda

• Introduction / Meet the Community - 10 minutes
• Building Knowledge Graphs on unstructured data using NLP libraries - 40 mins
• Q&A - 10 mins

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