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Description
Large language models are good at understanding and generating text, but they need reliable sources of information to provide correct answers to real business questions. A Knowledge Graph (KG) is a smart way to organize facts and show how everything is connected. It combines data from tables, documents, and even expert knowledge. Using a knowledge graph can help AI to focus on real facts, so it makes fewer mistakes and gives you answers you can trust. Another important point is that it reduces the likelihood of LLMs hallucinating, as the context provided helps to clearly define the domain of knowledge.
Expected Outcome
By creating a knowledge graph using best practices, you’ll get a clear and organized view of your data that shows how key concepts are connected. This helps the RAG system find more relevant information and give better answers.
The results include more accurate and trustworthy responses, fewer mistakes, and a system that’s easier to update and explain.
Key Concepts
Before you begin, it’s helpful to know a few key concepts. For example, an “entity” refers to a data element, and a “relationship” shows how elements are related to each other. Once you understand these concepts, working with knowledge graphs will become much easier.
Retrieval-Augmented Generation (RAG): Combines a search step with an LLM. Finds relevant info first, then uses it to generate better answers. You can find more how to use RAG in SAP BTP related best practice
Knowledge Graph (KG): A network of facts made up of entities (things like people or places) and relationships (how they’re connected).
Example Process Flow for a Vector Engine-powered RAG approach
RDF (Resource Description Framework) is a standard way to represent data as triples: subject, predicate, and object. The relationships encoded in triples are easy to understand.
Triple: The basic building block of a knowledge graph. In knowledge graph notation, facts are expressed as triples.
A triple has three parts: subject – predicate – object (e.g., “Compressor Motor A– IsMonitoredBy – Vibration Sensor X”)
Example Process Flow for a Vector Engine-powered RAG approach
Ontology: An ontology describes how the entities in your KG relate to each other. It’s a conceptualization of what those entities and relationships mean in that domain, and in the context of the related knowledge graph a representation of the types of entities, their properties, and the relationships between them. OWL (Web Ontology Language) is a powerful language for defining and representing these ontologies in a machine-readable format.
Entity Extraction: Pull out important names or terms from text (like “Paris” or “SAP BTP”)
Relation Extraction: Find how entities are related (e.g., “works at”, “founded by”, “related at”).
Normalization/Deduplication: The process of merging duplicate or similar entities to keep the graph clean and avoid redundancy.
Example: “IBM”, “I.B.M.”, and “International Business Machines” → one node
SPARQL is a query language for working with RDF data. You use SPARQL to search, filter, and get information from knowledge graphs built with RDF. Check SAP HANA Cloud Knowledge Graph Engine in SAP HANA Database. Read More SAP Developers
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