in Using Data Type Indexes chapter, documentation says "While data type indexes improve query performance, overhead from incremental index maintenance can degrade the performance of DML and bulk load operations on the semantic network. For bulk load operations, it may often be faster to drop data type indexes, perform the bulk load, and then re-create the data type indexes".
This makes sense. However, I can't drop the data type indexes on MDSYS.RDF_VALUE$ when bulk loading triples into a RDF model since other existing models still need to use them.
Is there any way to partition data type indexes by the RDF model to solve this?
Right now, it is not possible to partition data type indices by RDF models. And this is due to the fact that we store all URIs, literals, b-nodes in a sharedtable to facilitate querying across multiple RDF models (graphs, named graphs, inferred graphs). Since you cannot drop the data type indices (because they are being used by others), the bulk data loading performance won't be optimal. Hopefully it won't cause a big problem for your application.