📄️ Subgraphs and Embeddings
While the concepts behind the naive extraction process of TrustGraph are decades old, RAG is incredibly new in comparison. RAG is a rapidly evolving domain with “Graph RAG” being the latest buzzword. In fact, some might consider TrustGraph to be a “Graph RAG” solution. Until recently, the term RAG has referred to performing a semantic similarity search on vector embeddings which are typically linked to text statements stored in a table. The search returns a list of most similar indexes, which then retrieves the statements in the table. These statements are then fed into a LM for a generative response.
📄️ SPARQL Queries
Another benefit of aligning with RDF is SPARQL. SPARQL (pronounced “sparkle”) is a query language developed specifically for finding knowledge patterns stored in a RDF knowledge graph. SPARQL is also managed by the W3C with full details here. There’s an extremely large set of mature pattern extraction algorithms using SPARQL that are compatible with TrustGraph for extracting subgraphs.
📄️ Knowledge Reformatting
Despite the maturity of RDF and SPARQL, extracting a subgraph for the purpose of RAG is very new. Let’s look at a simple example: