Problem Statement 1

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Project: Domain-Specific FAQ Chatbot with Knowledge Graph Integration

Overview

Traditional chatbots often rely solely on predefined responses or general-purpose AI models, leading to shallow answers that lack domain-specific depth. They struggle to understand complex relationships, hierarchies, and contextual dependencies within a specialized field. This results in inaccurate or incomplete responses, making them unreliable for industry-specific use cases.

Challenge

Develop a domain-specific FAQ chatbot that:

• Integrates a knowledge graph for real-time contextual understanding.
• Understands relationships, hierarchies, and dependencies within a domain.
• Provides insightful, structured, and fact-enriched answers beyond standalone AI models.
• Supports real-time updates as new knowledge is added to the graph.
• Enhances responses with definitions, examples, and contextual references from structured data.

Key Features

• Conversational AI + Knowledge Graph: Combines NLP (LLM) with structured data.
• Context-Aware Answers: Leverages relationships and hierarchies for deeper insights.
• Real-Time Data Access: Fetches the latest domain-specific facts dynamically.
• Adaptive Learning: Updates the knowledge graph with new insights over time.
• Multi-Format Support: Responds with text, images, links, and interactive elements.

Impact

This chatbot will transform industry-specific FAQs by providing more accurate, contextual, and enriched responses. It will enable businesses to automate complex queries, improve customer support, and enhance knowledge management through AI-powered intelligence.

Learning outcomes

• Knowledge base querying with MeTTa
• MeTTa-Python integration
• Graph RAG (Retrieval-Augmented Generation)

Ready to Build It? 🚀

Take on the challenge of redefining chatbot intelligence with MeTTa’s graph integration. Show us how your chatbot can truly understand and adapt to your domain!🤖💡🔍