Large language models (LLMs) can be incredibly transformative for surfacing company data & insights. However, the path to enriching a company's knowledge base with LLMs remains uncharted.
This webinar explores the interaction between LLMs and Knowledge Graphs (KG). We first demonstrate how LLMs can transform unstructured text into a well-formed Knowledge Graph by extracting entities and relationships from a dataset. We discuss the added value of storing data within a KG and how companies can leverage them.
Next, we delve into how LLM applications can interact seamlessly with the structured knowledge within a knowledge graph. In this session, with the help of a business use case, we explore how this knowledge graph empowers LLM applications with enhanced capabilities and intelligent insights.
In the practical part of the session, we will present a business use case for building an LLM-based Question answering system using GPT-3.5-turbo and prompting it with a knowledge graph.
This webinar promises to illuminate how LLM applications can interact intelligently with structured knowledge for semantic understanding and reasoning.
Key Takeaways:
- How LLMs can unlock the potential of building organizational knowledge graphs.
- How knowledge graphs can further guide LLMs’ reasoning capabilities.
- How to use pretrained models for translating natural language queries to SPARQL queries on top of company knowledge graphs
Access the demo notebook: bit.ly/3sWSq0F
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