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World Congress 2024 • Aug 22, 2024 • Session details

Large Language Models ❤️ Knowledge Graphs

Michael Hunger

Fine-tuning LLMs is an expensive trap. Graph RAG is the solution. Learn to build explainable AI agents by grounding models in verifiable, highly connected knowledge graphs.

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#1 about 2 min

Introduction to generative AI and knowledge graphs

Combining language models with graph databases yields powerful approaches to organizing and processing enterprise data.

#2 about 2 min

Overcoming hallucination challenges in large language models

The tendency of base models to hallucinate necessitates anchoring them to specific organizational knowledge bases.

#3 about 2 min

Comparing fine-tuning to database retrieval and grounding

Grounding language models via accessible database context offers superior security and efficiency compared to full fine-tuning.

#4 about 2 min

Using retrieval-augmented generation for situational context

Passing relevant document fragments into response prompts restricts the search space and improves accurate completions.

#5 about 2 min

Structuring enterprise data utilizing knowledge graph databases

Viewing large-scale corporate data as interconnected nodes facilitates intuitive querying and effective digital twin modeling.

#6 about 3 min

Modeling public forum data with graph relationships

Mapping forum questions and users illustrates the practical scale and pattern-matching abilities of graph schemas.

#7 about 3 min

Extracting graph entities from unstructured text automatically

Leveraging base language intelligence extracts reliable structured semantic entities from unstructured text documents automatically.

#8 about 4 min

Demonstrating document ingestion and knowledge graph visualization

Integrating a document processing pipeline demonstrates how raw schedules become navigable nodes and vector embeddings.

#9 about 3 min

Combining vector search with structured graph traversal

Employing dual search methods enables applications to pinpoint entry facts and dynamically follow related patterns.

#10 about 6 min

Querying knowledge graphs and vector indexes via Python

Orchestrating operations via scripting integrates database drivers and models for explicit user query resolution.

#11 about 3 min

Adding explainability and topic clustering to applications

Tracking source links and applying mathematical clustering ensures reliable transparency and restricts untruthful model generations.

#12 about 1 min

Exploring educational resources for developing graph applications

Reviewing available platform tutorials and code repositories prepares engineering teams for successful architectural implementations.

#13 about 2 min

Validating extraction quality and directing graph creation

Crafting exact entity schemas and comparing automated outputs to human validation guarantees high data quality.

Matching moments

2:22 min

Unlocking generative AI capabilities using knowledge graphs

Zaid Zaim Zaid Zaim +1 · WWC Europe 2026

2:26 min

Enhancing language models with graph retrieval augmented generation

2:05 min

Enhancing language models with retrieval-augmented generation

Mary Grygleski Mary Grygleski · LIVE

4:31 min

Combining knowledge graphs with LLMs for reliability

Stephen Chin Stephen Chin · WWC 2024

1:53 min

Overcoming language model challenges using retrieval-augmented generation

Ashish Sharma · LIVE

3:29 min

Mitigating artificial intelligence hallucinations with constraints and context

Jemiah Sius Jemiah Sius · WWC 2024

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