[spothedog1]

Looks cool, would love support for RDF Graphs. The reason I prefer those is because the ontology is already well defined in a lot of cases which is 80% of the battle with Knowledge Graphs in my experience. Without a well defined Ontology I think LLM <> KG integration will not live up to its potential. LLMs have to know what nodes and edges really mean across diverse datasets

[prasmuss15]

Hey, thanks for the feedback! I'm one of the devs on graphiti and adding support for custom schema is high on our to-do list. I agree that this is an important step in helping to bridge the gap between structured and unstructured data, as well as for refining the graph on specific use cases.

Currently, we do have some ways of helping the graph to understand what nodes and edges "really mean." In addition to the name of the relationship our edges also store a "hydrated" version of the fact triple. For example, if Alice and Bob are siblings you might see an edge with the name IS_SIBLING_OF between the two. In addition to this, the edge also stores the fact: "Alice is the sibling of Bob". This way we are storing much of the semantic context on the nodes and edges themselves in addition to the graph structure.

We also support ingesting structured JSON, and I those cases the edges will be exactly the properties in the JSON doc.

[spothedog1]

The reason I bring RDF is because I use ontologies that have been defined by experts and covers ton of edge cases. If a group of genealogists define a `fam:` RDF Ontology and publish it, then I want every family relationship in my graph to use their Ontology.

I'm looking for something like graphiti that can take in a text block and when creating the relationships, automatically know to use the `fam:` ontology when creating familial relationships. The vast majority of people don't feel like defining schemas for every little thing and they're basically the same across all systems except for custom proprietary ones you define as your IP.

Their ontology would have OWL rules like `fam:isChildOf` `owl:inverseOf` `fam:isParentOf` so running an OWL Reasoner over the graph would generate the inverse triples as well

So if I had the text `Joe is Bob's dad`, input it into graphiti, then get the triples

person:Joe fam:isParentOf person:Bob person:Bob fam:isChildOf person:Joe

and the edge would be in a shared definition amongst all graphiti users. The LLM can be fine tuned to recognize exactly what fam:isParentOf means so there is no ambiguity. Right now I'm guessing graphiti could spit out edges `IS_SIBLING_OF` `SIBLING` `SISTER` `BROTHER` etc, its not standardized which makes it difficult to interact with computationally if say, I wanted to input a bunch of random text and then run pre-trained graph models of family networks.

[chatmasta]

Have you seen TerminusDB? [0] They’ve got a nice solution to versioned RDF graphs, originally pitched as “Git for data” but focused on knowledge graphs.

I’m not affiliated (in fact they launched around the same time that my co-founder and I launched Splitgraph with the same “Git for data” pitch), but I find their technology very intriguing.

Knowledge graphs are on the cusp of revival after being in stasis for 20 years. They’re a perfect match for LLMs and I’m excited to see how the field adopts them.

[0] https://terminusdb.com/