I'm working on a knowledge graph project using Stardog and I'm currently facing challenges in designing a complex SPARQL query that needs to access and reason across multiple named graphs.
Here’s the scenario: I have different named graphs representing various domains — e.g., :Products, :Customers, and :Transactions. Each named graph holds RDF data specific to its domain. My goal is to write a query that performs reasoning (using OWL RL rules) and links data across these named graphs. For instance, I want to find all customers who purchased a product that belongs to a certain category, and enrich that query using some inferred relationships.
What’s tripping me up is understanding how Stardog handles reasoning in the context of named graphs. Does reasoning apply within each graph individually, or is there a way to reason across all graphs globally? Also, when using FROM NAMED clauses, does the inference engine still kick in, or do I need to use some other construct?
I’ve read through the documentation on virtual graphs and reasoning, but I’m still unclear on best practices for querying when multiple graphs and inference are involved. If anyone has experience with similar use cases, sample queries, or tips for optimizing performance of rpa training in chennai, I’d really appreciate your input.
Stardog supports schema desynchronization which allows for multiple data models to be used across the same data. Because of this structure, Stardog reasoning queries need to two pieces of information when applying reasoning: the graph scope (named graphs for the data) and the data model. Stardog assumes a default graph and data model scope when not provided.
These can be defined in the settings for Voicebox and Explorer or through the toolbar in Studio's workspace (examples shown below) or provided in the query parameters (query execute | Stardog Documentation Latest).
I suggest we first figure out how your query should link data residing in different named graphs and then, once the first step works, make it use reasoning.
You are correct that FROM NAMED is one method to use when there're multiple named graphs. If you use that, you should use the graph <<IRI or variable>> { ... } construct to match the data. In that case, the query will match data in each named graph individually. See our early blog post on FROM vs FROM NAMED for more details.
Once that is sorted out, you can enable reasoning. For that, you should first think where your schema is. It can be in a single named graph or in multiple named graphs, but the schema for the given query is always treated as a single collection of rules or axioms. However, if the query matches data in each named graph separately, it will still be true with reasoning (i.e. you'll get inferences for each NG).
If you want to match data or infer something across named graphs, the pattern in your query should be in the same scope. For example:
SELECT *
FROM NAMED <urn:products>
FROM NAMED <urn:customers>
{
graph <urn:products> { ... }
graph <urn:customers> { ... }
}
matches data in the two graphs independently whereas
SELECT *
FROM <urn:products>
FROM <urn:customers>
{
?product a :Product .
?order a :Order ; :forProduct ?product ; :hasCustomer ?customer .
}
would match data across both graphs. You would use this with reasoning if, for example, you have a rule whose body includes conditions to match in both customer and product graphs.
If you have troubles getting this to work, you may come up with a toy data example (with multiple NGs), and we might be able to help with that.
