Query engines have made retrieving data on RDF-interfaces seamless by abstracting away complexities such as heterogeneous interfaces and access-path dependencies. Techniques like link traversal and interface descriptions allow engines to dynamically discover and retrieve data, making querying more efficient and flexible.
However, while reading data has been significantly simplified, updating data remains a major challenge - specifically because of the access path data-dependence. Current query engines require explicit instructions on how updates should be performed for each data-specific interface, which undermines the abstraction power that query engines typically provide.
One promising approach to addressing this challenge is leveraging provenance trails. Since updates are typically expressed in relation to existing data, query engines—which excel at locating relevant data—could infer where newly created data should be stored by tracking the sources that contributed to its creation.