Description

Introduction
Chatbots become significantly more intelligent and context-aware when integrated with knowledge graphs. By leveraging structured relationships between entities, chatbots can provide precise answers, maintain memory across interactions, and reason about complex topics. This course explores how to integrate knowledge graphs with conversational AI to enable dynamic, intelligent, and human-like interactions.

Prerequisites

• Basic understanding of chatbot frameworks (e.g., Dialogflow, Rasa, or Botpress)

• Familiarity with graph databases (e.g., Neo4j) and SPARQL or Cypher queries

• Foundational knowledge of NLP and intent recognition

• Experience with Python or JavaScript

Table of Contents

1. Foundations of Knowledge Graphs
1.1 What Is a Knowledge Graph?
1.2 Entities, Relationships, and Ontologies
1.3 Common Formats: RDF, OWL, JSON-LD
1.4 Use Cases in Search, Recommendation, and Chatbots

2. Introduction to Chatbots
2.1 Recap: Rule-based vs AI-powered Bots
2.2 NLU, Intent Detection, and Dialogue Management
2.3 Contextual Conversations and Session Handling
2.4 Integrating External Data Sources

3. Bridging Chatbots with Knowledge Graphs
3.1 Designing Queryable Knowledge Structures
3.2 Integrating Chatbot NLU with Graph Queries
3.3 Entity Recognition and Disambiguation Using KG
3.4 Dynamic Responses from Graph-Based Search

4. Tools & Frameworks
4.1 Using Neo4j with Dialogflow or Rasa
4.2 SPARQL and Cypher in Conversation Flows
4.3 Embedding KG Lookups in Middleware
4.4 Open-source Libraries and APIs

5. Real-World Applications
5.1 Customer Support with Context-Aware Answers
5.2 Knowledge-driven FAQs and Auto-Suggestions
5.3 Conversational Search Assistants
5.4 Enterprise Use Cases: HR, ITSM, Product Support

6. Advanced Concepts
6.1 Combining KG with Embedding-based Retrieval
6.2 Linked Data and Federated Knowledge
6.3 Real-Time Graph Updates and Versioning
6.4 Reasoning and Inference in Conversations

7. Challenges & Best Practices
7.1 Performance Considerations with Large KGs
7.2 Managing Incomplete or Noisy Data
7.3 UX Design for Knowledge-Heavy Conversations
7.4 Privacy, Security, and Compliance in Graph-Based Bots

Combining chatbots with knowledge graphs takes conversational AI to the next level—offering smarter, more informed, and contextual interactions. By understanding how to structure, query, and integrate knowledge, developers and designers can build assistants that truly understand and guide users effectively. This integration is key to building the intelligent agents of tomorrow.