Knowledge Graph & LLM Application - Knowledge graphs combined with large language models (LLMs) can be used to build AI applications, particularly with graph retrieval augmented generation (RAG) architecture [2] - Graph RAG replaces vector databases with knowledge graphs built on graph databases, enhancing retrieval strategies [3] - Using a knowledge graph provides richer retrieval strategies beyond vector semantic search, including contextualization and structured queries [4] - Property graph model implements nodes and relationships, nodes represent entities and relationships connect them [4][5] Ontology & Schema - Ontologies provide an implementation-agnostic approach to representing schemas, facilitating knowledge graph creation for both structured and unstructured data pipelines [14][17] - Ontologies describe a domain with definitions of classes and relationships, matching well with graph models [15] - Financial Industry Business Ontology (FIBO) is a public financial industry ontology example [15] - Storing ontologies in the graph can drive dynamic behavior in retrievers, allowing for on-the-fly adjustments by modifying the ontology [29][30] Retrieval Strategies - Graph captures text chunks with embeddings, creating a new search space for vector search [20] - Vector search finds vectors in proximity, which can be dereferenced back to the graph for contextualization, navigation, and enrichment [20] - Dynamic queries, driven by ontologies, can be used to create dynamic retrievers, enabling data-driven behavior [26][29]