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World Congress 2023 • Oct 6, 2023

Knowledge graph based chatbot

Tomaz Bratanic

Are token limits and hallucinations breaking your standard RAG? Discover how integrating Neo4j knowledge graphs enables deterministic, multi-hop chatbots that flawlessly execute complex logic.

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#1 about 3 min

Common limitations of natural language processing models

Native model challenges include strict knowledge cutoffs, factual hallucinations, and a lack of domain-specific private data.

#2 about 2 min

Extending model capabilities through external framework integrations

Using plugins and development frameworks allows text models to interact dynamically with real-time and private service information.

#3 about 2 min

Solving context problems with retrieval augmented generation

Injecting relevant external text into dynamic prompts prevents hallucinations and enables accurate system source citation.

#4 about 3 min

Using vector embeddings for unstructured text search

Converting raw text chunks into dense numeric arrays enables semantic similarity comparisons for retrieving document context.

#5 about 2 min

Representing structured domain data within knowledge graphs

Mapping complex application domains into interconnected nodes and lines provides an architecture built for explicit logical querying.

#6 about 3 min

Generating graph database queries with language models

Translating user intent directly into Cypher syntax retrieves exact contextual database records instead of noisy textual passages.

#7 about 2 min

Handling multi-hop question answering with connected entities

Compiled graph database queries easily resolve complex logic patterns requiring traversal across multiple distinct conceptual relationships.

#8 about 5 min

Extracting and querying domain specific knowledge graphs

Structuring raw documents during initial data ingestion creates scalable property graphs supporting complex hardware or system architectures.

#9 about 5 min

Demonstrating a knowledge graph powered chatbot interface

A customized client engine executes backend Cypher lookups to map relationships and summarize internal engineering news.

#10 about 2 min

Approaches for data extraction and fuzzy entity matching

Handling unstructured text feeds effectively involves named extraction techniques and relying on natural language understanding to correct misspellings.

Matching moments

1:44 min

Introduction to generative AI and knowledge graphs

Michael Hunger Michael Hunger · World Congress 2024

2:22 min

Unlocking generative AI capabilities using knowledge graphs

Zaid Zaim Zaid Zaim +1 · World Congress 2026 Europe

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

2:08 min

Traversing entity relationships with graph memory

Elizabeth Fuentes Leone Elizabeth Fuentes Leone · World Congress 2026 North America

1:44 min

Understanding basic retrieval-augmented generation architectures in chatbots

Stan Girard Stan Girard · World Congress 2024

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