Building Sustainable GenAI Systems: The Critical Role of Knowledge Graphs and Ontologies
A Talk by Anthony Alcaraz (Senior AI/ML BD/Strategist Manager EMEA, AWS)
About this Talk
As enterprises rush to adopt generative AI, many are overlooking a critical foundation needed for long-term success and scalability: knowledge graphs and ontologies. In this workshop, Anthony Alcaraz, Senior Strategist and BD AIML at AWS, will explain why this "knowledge stack" is essential for creating truly sustainable and impactful generative AI systems.
Generative AI models excel at producing human-like text, images, and other content. However, they often lack the structured knowledge and reasoning capabilities needed to consistently generate accurate, contextually relevant, and trustworthy outputs for enterprise applications. This is where knowledge graphs and ontologies become crucial.
Knowledge graphs provide a flexible, interconnected representation of domain knowledge, while ontologies define the concepts, relationships, and rules governing that knowledge. Together, they form a powerful knowledge foundation that can be leveraged to enhance generative AI systems in several key ways:
- Grounding AI outputs in factual knowledge
- Enabling multi-hop reasoning and inference
- Providing explanations and provenance for AI-generated content
- Facilitating data integration across enterprise silos
- Ensuring consistency and reducing hallucinations
This workshop will explore how leading companies are integrating knowledge graphs and ontologies with large language models and other generative AI technologies to build more robust, explainable, and trustworthy AI systems. We'll examine real-world use cases, discuss implementation strategies, and provide guidance on getting started with this critical knowledge stack.
Furthermore, we'll delve into the emerging bridges between LLM reasoning and graph-based reasoning. As these two powerful paradigms converge, we can expect to see AI systems that combine the flexibility and natural language understanding of LLMs with the structured knowledge and logical inference capabilities of graph-based approaches. This convergence promises to unlock new levels of AI performance and applicability in enterprise settings, potentially revolutionizing how we approach complex problem-solving and decision-making processes.
By attending this workshop, participants will gain a comprehensive understanding of how to build more robust, explainable, and trustworthy AI systems that can drive real business value. They'll leave equipped with practical strategies for implementing knowledge-enhanced AI in their own organizations and a vision for the future of enterprise AI that leverages the best of both symbolic and neural approaches.
Key Topics
- The limitations of standalone generative AI models for enterprise applications
- How knowledge graphs and ontologies address these limitations
- Architectures for integrating knowledge graphs with generative AI
- Tools and technologies for building enterprise knowledge graphs
- Best practices for ontology development and management
- Measuring the impact of knowledge-enhanced generative AI
- Future directions: multi-modal knowledge graphs and reasoning
- Emerging bridges between LLM reasoning and graph-based reasoning
Target Audience
- Chief Data Officers and Chief AI Officers
- Enterprise Architects
- Data Scientists and ML Engineers
- Knowledge Management Professionals
- Business Leaders interested in sustainable AI adoption
Goals
- Understand why knowledge graphs and ontologies are essential for sustainable generative AI
- Learn strategies for integrating structured knowledge with generative models
- Explore real-world use cases and success stories
- Gain practical insights on implementation and best practices
- Identify next steps for leveraging knowledge graphs in your AI initiatives
- Understand the potential of combined LLM and graph-based reasoning approaches
Session Outline
- Introduction (15 minutes)
- The promise and limitations of generative AI
- The knowledge gap in current AI systems
- Knowledge Graphs and Ontologies: A Primer (20 minutes)
- What are knowledge graphs and ontologies?
- How they complement and enhance generative AI
- Architectural Patterns (30 minutes)
- Retrieval-augmented generation (RAG) with knowledge graphs
- Ontology-guided prompt engineering
- Hybrid neural-symbolic systems
- Implementation Strategies (25 minutes)
- Building enterprise knowledge graphs: sources, tools, and techniques
- Ontology development and management best practices
- Integration challenges and solutions
- Case Studies (15 minutes)
- Healthcare
- Law
- Bridging LLM and Graph Reasoning (20 minutes)
- Current limitations in LLM and graph-based reasoning
- Emerging techniques for combining LLM and graph capabilities
- Graph-augmented language models
- Language model-guided graph traversal and inference
- Potential impact on enterprise AI applications
- Getting Started (15 minutes)
- Assessing your organization's readiness
- Building a roadmap for knowledge-enhanced AI
- Key success factors and potential pitfalls
- Q&A and Discussion (20 minutes)
Format
- Interactive workshop
- 2.5 hours running time
- Mix of presentations, demonstrations, and group discussions
Level
- Intermediate to Advanced
Prerequisite Knowledge
- Basic understanding of AI/ML concepts
- Familiarity with enterprise data management challenges