Exploring SAP Knowledge Graph

SAP Business Suite powers AI agents across SAP with unique with insights across your enterprise organization, making it essential for transforming and staying ahead in today's dynamic environment. The right context for this transformation is provided by SAP Knowledge Graph. In this lesson, we will show how SAP Knowledge Graph helps in grounding AI agents in the right context.

SAP Knowledge Graph enhances the integration and understanding of complex business data within SAP systems. It grounds LLMs by providing explicit meanings and relationships between entities, reducing inaccuracies and hallucinations in AI outputs.

SAP Knowledge Graph enables natural language queries on the structured, tabular world of SAP and non-SAP applications. This integration is necessary because LLMs face multiple challenges.

• Naive Retrieval-Augmented Generation (RAG) techniques based on vectorized data have many limitations.
• They often overlook relationships between entities within texts.
• LLMs also struggle to access enterprise data and understand complex formats, hierarchies, or domain-specific knowledge. This can result in more hallucinations and a lack of understanding of SAP's metadata model.

SAP Knowledge Graph tackles these challenges by making the meaning and relationships between entities explicit. This helps ground LLMs in SAP's entire semantic model. Combining new techniques like GraphRAG with vector-based RAG and business data stored in SAP HANA and SAP Datasphere opens a new chapter in using data powerfully.

To illustrate the size of SAP's metadata model, the SAP S/4HANA knowledge graph is based on 452,000 ABAP tables, 80,000 CDS views, and 7.3 million fields.

This powerful engine is set to supercharge AI models, making enterprise-ready AI experiences much faster to build. Making implicit knowledge explicit is a superpower.

The Right Context for AI Agents

SAP Knowledge Graph helps bridge the gap for AI between complex data structures and natural language allowing AI-driven insights and enhanced decision-making capabilities.

SAP Knowledge Graph provides essential context to AI agents by encoding SAP's unique expert knowledge on business processes and data. This grounding enables AI agents to seamlessly navigate relevant business process knowledge and data products, allowing them to solve more complex problems effectively. Here are some key ways in which SAP Knowledge Graph provides context to AI agents:

1. Understanding Complex Problems: SAP Knowledge Graph helps AI agents understand the full context of complex problems by disambiguating terms, linking concepts to entities, and grounding alerts or user conversations in domain-specific knowledge.
2. Decision-Making: SAP Knowledge Graph enables AI agents to evaluate multiple paths forward, prioritize those paths based on business constraints, and ensure outcomes align with insights gathered from SAP Business Data Cloud.
3. Reasoning and Sharing Findings: AI agents use SAP Knowledge Graph to infer connections, validate assumptions, or derive logical conclusions from structured relationships. This helps them reach accurate conclusions and provide recommendations.
4. Executing Resolutions: SAP Knowledge Graph allows AI agents to activate the appropriate agents to execute specialized tasks across business functions, ensuring a transparent and repeatable process.

In summary, SAP Knowledge Graph is a powerful tool that helps connect complex data with AI-driven insights. By making hidden knowledge clear, it enables AI agents to understand and solve complicated business problems more effectively. This leads to better decision-making, smarter reasoning, and smoother execution of tasks across various business functions.